Systems and methods for quantum based optimization of a personalized portfolio

ABSTRACT

Various systems and methods are provided for quantum computing based optimization of a personalized portfolio. One exemplary method may comprise identifying one or more filtered personalized portfolio optimization factor data based on one or more optimization factor data for the personalized portfolio, personalized portfolio owner feedback, QC algorithms, and algorithm performance information, selecting one QC algorithm for each filtered portfolio optimization factor data of the one or more filtered portfolio optimization factor data, utilizing the selected QC algorithm to optimize a personalized portfolio determination for each identified filtered personalized portfolio optimization factor data, and rebalancing the personalized portfolio based on the personalized portfolio determination.

TECHNOLOGICAL FIELD

Example embodiments of the present disclosure relate generally tooptimization and, more particularly, to systems and methods for quantumalgorithm-based optimization.

BACKGROUND

Although still in its infancy, quantum computing and its boundlesspotential applications are of rapidly increasing interest to a broadarray of industrial sectors, including simulation, artificialintelligence, healthcare, and financial services. Unlike classicalcomputers, which process information in bits that can only represent oneof two binary information states at a time, quantum computers processinformation in quantum bits (qubits) that can represent a coherentsuperposition of both binary information states at the same time.Further, two or more qubits may be entangled so that their physicalproperties are correlated even when separated by large distances, andquantum computers may simultaneously perform a vast number of operationson these entangled qubits. This massive parallelism allows quantumcomputers to perform incredibly complex calculations at speedsunimaginable today and solve certain classes of problems that are beyondthe capability of today's most powerful supercomputers.

Reflecting this broad potential impact, companies from a variety ofmarket sectors are investing substantial resources to develop thesepromising quantum computing theories into real-world quantum computingcapabilities.

BRIEF SUMMARY

Computing systems, computing apparatuses, computer-implemented methods,and computer program products are disclosed herein for using quantumcomputing (QC) to optimize performance.

In some embodiments, a system may be provided for quantum computing (QC)based optimization of a personalized portfolio, the system comprising:QC optimization factor filtering circuitry configured to identify one ormore filtered personalized portfolio optimization factor data based onone or more optimization factor data for the personalized portfolio,personalized portfolio owner feedback, QC algorithms, and algorithmperformance information; algorithm selection circuitry configured toselect one QC algorithm for each filtered portfolio optimization factordata of the one or more filtered portfolio optimization factor data; QCoptimization circuitry configured to utilize the selected QC algorithmto optimize a personalized portfolio determination for each identifiedfiltered personalized portfolio optimization factor data; and processingcircuitry configured to rebalance the personalized portfolio based onthe personalized portfolio determination.

In some embodiments, the system further comprises input-output circuitryconfigured to receive portfolio owner constraints related to thepersonalized portfolio, and wherein the portfolio owner constraints areincluded in the personalized portfolio owner feedback.

In some embodiments, the system further comprises input-output circuitryconfigured to provide a personalized portfolio owner with a rebalancingalert based on the personalized portfolio determination.

In some embodiments, the system further comprises processing circuitryconfigured to, after receiving additional feedback from the personalizedportfolio owner, rebalance the personalized portfolio associated withthe personalized portfolio owner based on the additional feedback andthe personalized portfolio determination.

In some embodiments, the algorithm selection circuitry is furtherconfigured to receive a catalog of QC algorithms and associatedalgorithm performance information.

In some embodiments, the optimization factor data comprises one or moreof personal portfolio constraints data representing customer-definedconstraints.

In some embodiments, the algorithm selection circuitry configured toselect one QC algorithm is further configured to select QC algorithmsbased on a QC run cost.

In some embodiments, a method may be provided for QC based optimizationof a personalized portfolio, the method comprising: identifying one ormore filtered personalized portfolio optimization factor data based onone or more optimization factor data for the personalized portfolio,personalized portfolio owner feedback, QC algorithms, and algorithmperformance information; selecting one QC algorithm for each filteredportfolio optimization factor data of the one or more filtered portfoliooptimization factor data; utilizing the selected QC algorithm tooptimize a personalized portfolio determination for each identifiedfiltered personalized portfolio optimization factor data; rebalancingthe personalized portfolio based on the personalized portfoliodetermination.

In some embodiments, the method further comprises receiving portfolioowner constraints related to the personalized portfolio, and wherein theportfolio owner constraints are included in the personalized portfolioowner feedback.

In some embodiments, the method further comprises providing apersonalized portfolio owner with a rebalancing alert based on thepersonalized portfolio determination.

In some embodiments, the method further comprises, after receivingadditional feedback from the personalized portfolio owner, rebalancingthe personalized portfolio associated with the personalized portfolioowner based on the additional feedback and the personalized portfoliodetermination.

In some embodiments, the method further comprises receiving a catalog ofQC algorithms and associated algorithm performance information.

In some embodiments the optimization factor data comprises one or moreof personal portfolio constraints data representing customer-definedconstraints.

In some embodiments, the selecting one QC algorithm is based on a QC runcost.

In some embodiments, a computer program product is provided for QC basedoptimization of a personalized portfolio, the computer program productcomprising at least one non-transitory computer-readable storage mediumstoring program instructions that, when executed, cause a system to:identify one or more filtered personalized portfolio optimization factordata based on one or more optimization factor data for the personalizedportfolio, personalized portfolio owner feedback, QC algorithms, andalgorithm performance information; select one QC algorithm for eachfiltered portfolio optimization factor data of the one or more filteredportfolio optimization factor data; utilize the selected QC algorithm tooptimize a personalized portfolio determination for each identifiedfiltered personalized portfolio optimization factor data; and rebalancethe personalized portfolio based on the personalized portfoliodetermination.

In some embodiments, the program instructions, when executed, furthercause the system to receive portfolio owner constraints related to thepersonalized portfolio, and wherein the portfolio owner constraints areincluded in the personalized portfolio owner feedback.

In some embodiments, the program instructions, when executed, furthercause the system to provide a portfolio owner with a rebalancing alertbased on the personalized portfolio determination.

In some embodiments, the program instructions, when executed, furthercause the system, to receive a catalog of QC algorithms and associatedalgorithm performance information.

In some embodiments, the portfolio optimization factor data comprisesone or more of personal portfolio constraints data representingcustomer-defined constraints.

In some embodiments, the program instructions, when executed, furthercause the system, to select one QC algorithm for each filteredpersonalized portfolio optimization factor data based on a QC run cost.

The foregoing brief summary is provided merely for purposes ofsummarizing some example embodiments illustrating some aspects of thepresent disclosure. Accordingly, it will be appreciated that theabove-described embodiments are merely examples and should not beconstrued to narrow the scope of the present disclosure in any way. Itwill be appreciated that the scope of the present disclosure encompassesmany potential embodiments in addition to those summarized herein, someof which will be described in further detail below.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying figures, which are not necessarily drawn to scale,illustrate embodiments and features of the present disclosure. Togetherwith the specification, including the brief summary above and thedetailed description below, the accompanying figures serve to explainthe embodiments and features of the present disclosure. The componentsillustrated in the figures represent components that may or may not bepresent in various embodiments or features of the disclosure describedherein. Accordingly, some embodiments or features of the presentdisclosure may include fewer or more components than those shown in thefigures while not departing from the scope of the disclosure.

FIG. 1 illustrates a system diagram of a set of devices that may beinvolved in some example embodiments described herein;

FIG. 2 illustrates a schematic block diagram of example circuitries thatmay perform various operations in accordance with some exampleembodiments described herein; and

FIG. 3 illustrates an example of a flowchart for optimizing anoptimization determination based on QC algorithms in accordance withsome example embodiments described herein.

FIG. 4 illustrates an example of an efficient frontier in accordancewith some example embodiments described herein.

FIG. 5 illustrates an example of a flowchart for an efficient frontierdetermination based on QC algorithms in accordance with some exampleembodiments described herein.

FIG. 6 illustrates an example of a flowchart for a tracking errordetermination based on QC algorithms in accordance with some exampleembodiments described herein.

FIG. 7 illustrates an example flowchart for a stress testingdetermination based on QC algorithms in accordance with some exampleembodiments described herein.

FIG. 8 illustrates an example flowchart for a hurricane determinationbased on QC algorithms in accordance with some example embodimentsdescribed herein.

DETAILED DESCRIPTION

Some embodiments of the present disclosure will now be described morefully hereinafter with reference to the accompanying figures, in whichsome, but not all embodiments of the disclosures are shown. Indeed,these disclosures may be embodied in many different forms and should notbe construed as limited to the embodiments set forth herein; rather,these embodiments are provided so that this disclosure will satisfyapplicable legal requirements. Like numbers refer to like elementsthroughout.

Overview

As noted above, methods, apparatuses, systems, and computer programproducts are described herein for using quantum computing (QC) tooptimize performance of a determination related to an asset, a portfolioof assets, or a company. In some embodiments, the performance of thedetermination is being optimized using QC. QC and applicationperformance monitoring (APM) may be used to identify factors related tothe determination for optimization using a QC algorithm, for example,Quadratic Unconstrained Binary Optimization “QUBO”, Quantum ApproximateOptimization Algorithm “QAOA”, Quantum Machine Learning “QML”, QuantumGeometrodynamics “QGD”, Quantum Monte Carlo “QMC”, Harrow, Hassidim andLloyd “HHL”, or the like, executed on a particular QC machine (e.g.,quantum annealer, circuit-based quantum processor, or the like).

Example embodiments may select the particular QC algorithm and QCmachine for optimizing a determination. For example, example embodimentsmay run quantum optimization on the identified factors to determinewhich are best for using a particular QC algorithm and QC machine (e.g.,use a quantum annealer and related algorithm on a first factor or asset,use a circuit-based quantum processor and related algorithm on a secondfactor or asset). In another example, example embodiments may testdifferent QC algorithms and QC machines to generate a matrix of QCperformance information for use in selecting the optimal QC algorithmand QC machine for each identified factor or asset. In yet anotherexample, example embodiments may determine what is the best path in thesequence for QC and use a combination of different QC algorithms and QCmachines for that path.

The determination being optimized may be for different applications. Forexample, a determination may be related to portfolio optimization,efficient frontier, tracking errors, stress testing, and/or hurricanegraphics, all of which may share factors and/or may have distinctfactors. For example, portfolios may include numerous assets, including,but not limited to, cash, cash deposits, stocks, bonds, real estate,mortgages, commodities, futures, options, loans, mutual funds,collateral, currencies, etc. Furthermore, these portfolios may bedesigned to achieve set goals, including, but not limited to, maximizingreturns, maximizing returns at a certain level of risk, reaching atarget goal, reaching a target goal at a target time or age, reach atargeted rate of return each year, maintain an amount in low risk assetswhile maintaining the remainder in a assets of a different risk level,track a benchmark, pass a stress test, pass a hurricane scenario, etc.Given the number of factors that may be considered (e.g., types ofasset, budget constraints, holding time, risk tolerance, etc.), theextensive number of assets held in a portfolio, and the ever-changingstate of the market as well as its effect on each of the assets,financial modeling and optimization algorithms are often used to beoptimize a determination regarding a portfolio (e.g., number and type ofassets held). By way of example, a portfolio may be concerned withmaximizing returns while also minimizing risk. As is evident by thenumber and/or types of assets in a portfolio, the number of simulationsand calculations that must occur, for example, for each potentialpermutation of the portfolio's assets in order to optimize adetermination, such as performance of the portfolio, may exceed thecapability of conventional computing.

Some attempts directed at accomplishing these calculations have reliedupon machine learning and other dynamic programming techniques (e.g.,reinforcement learning) to train a model that maximizes cumulativereward (e.g., maximizing return). Emerging computing technology in thespace of Quantum Computing further illustrate promise due to theirability to perform a significant number of complex calculations in ashorter time period than traditional computers. Furthermore, therandomly deterministic nature of quantum computing matches the goal ofportfolio optimization in which outcomes and performance of a particularportfolio are evaluated under a large number of randomly generatedscenarios.

In another example, the optimization may be related to post-quantumcryptography (PQC). Traditionally, data owners and third-party hostingservices use hybrid cryptosystems to safeguard the confidentiality,integrity, and authenticity of enormous volumes of protected data andcomplex IT systems. These hybrid cryptosystems typically use acombination of asymmetric cryptography (e.g., public key cryptography),such as the Rivest-Shamir-Adleman (RSA) cryptosystem, and symmetriccryptography (e.g., secret key cryptography), such as the AdvancedEncryption Standard (AES). One example of a hybrid cryptosystem is theTransport Layer Security (TLS) protocol, which relies on asymmetriccryptography for authentication and key management to establish sessionkeys, and symmetric cryptography for session encryption and integrityvalidation. This may or may not be different from hybrid mode approachto PQC, wherein a tool in hand (e.g., a digital certificate) is acombination of tradition cryptography, such as RSA, and post quantumcryptography, such as Dilithium, and such a hybrid mode approach may bereferred to as a hybrid scheme.

However, these cryptosystems are vulnerable to quantum algorithmsimplemented on quantum computers. For instance, asymmetric encryption,key exchange, and digital signature rely on mathematical problems suchas the integer factorization problem (e.g., as used in RSA) and thediscrete logarithm problem (e.g., as used in Digital Signature Algorithm(DSA), Elliptic Curve DSA (ECDSA), Diffie-Hellman (DH), and EllipticCurve DH (ECDH)). It is widely believed that a large-scale faulttolerant quantum computer could effectively break modern public keycryptosystems by solving instances of the integer factorization problemand the discrete logarithm problem quickly enough that keys reverseengineered based on those solutions would still be valid.

In one illustrative example, a quantum computer implementing Shor'salgorithm could determine the private keys used for current public-keysystems in a relatively short time because Shor's algorithm provides afaster cryptanalysis method for solving integer factorization than abrute force method (e.g., guessing prime numbers). For instance, Shor'salgorithm uses the quantum Fourier transform (QFT) instead of its slowerclassical counterpart, the fast Fourier transform (FFT). Further, Shor'salgorithm can be modified to compute discrete logarithms, includingdiscrete logarithms used for elliptic-curve cryptography (ECC).

In another illustrative example, a quantum computer implementingGrover's algorithm could effectively perform an exhaustive key searchbecause Grover's algorithm provides quadratic speedup and thereby couldbrute-force attack an N-bit symmetric cryptographic key in only about2^((N/2)) iterations. In some instances, for symmetric cryptographictechniques that support a doubled key length (e.g., AES supportsdoubling a 128-bit key to 256 bits), doubling the key length of thesymmetric cryptographic key may provide sufficient protection againstGrover's algorithm because a brute-force attack on a 2N-bit symmetriccryptographic key would require about 2^(N) iterations. For example, a256-bit symmetric cryptographic key (e.g., AES-256) may only provide 128bits of security in a quantum computing environment. However, anymigration plan that involves doubling the key length of the symmetriccryptographic key must also evaluate the impact of the doubled keylength on the performance of related applications and the additionalrequirements of computational resources.

Although quantum computers capable of such feats are still believed tobe several years away, the threat of a “harvest now and decrypt laterattack” makes quantum computing an immediate real threat, even if thethreat will not be actionable until a sufficiently robust quantumcomputer is developed in the future. The “harvest now and decrypt laterattack” is a long-game attack where a bad actor scrapes, collects, orharvests (e.g., records and stores) encrypted data, such as datastreaming through the Internet or cloud, by the way of breaches orpassive interception and then hoards the encrypted data, waiting for theday when quantum computers can determine the cryptographic keys to theharvested data. This bad actor could be storing data to or from aspecific website, server, email client, or other target of attack or,given sufficient motivation and resources, recording petabytes of dataeach hour from general internet traffic. Once quantum computers arecapable of determining the cryptographic keys associated with theharvested encrypted data, the bad actor might use those cryptographickeys to decrypt the previously encrypted data. For instance, persistentdata, such as mortgage information and financial records, encrypted ordigitally signed with today's cryptographic algorithms will be at riskeven if the necessary quantum computing technology is not available forseven to ten years or even later. Subsequently, with advancements inartificial intelligence and machine learning and the exponentialincrease in data processing compute power, a bad actor could attack adata vault to extract meaningful information from the decryptedpetabytes of data. The code may be related to providing post-quantumcryptography (PQC) that mitigates the vulnerability of traditionalcryptographic algorithms by providing techniques for migrating enormousvolumes of data and complex IT systems to PQC technologies and platformsthat are not vulnerable to attack by a quantum computer.

Definitions

As used herein, the terms “data,” “content,” “information,” “electronicinformation,” “signal,” “command,” and similar terms may be usedinterchangeably to refer to data capable of being transmitted, received,and/or stored in accordance with embodiments of the present disclosure.Thus, use of any such terms should not be taken to limit the spirit orscope of embodiments of the present disclosure.

The term “comprising” means “including, but not limited to.” The termcomprising should be interpreted in the manner it is typically used inthe patent context. Use of broader terms such as comprises, includes,and having should be understood to provide support for narrower termssuch as consisting of, consisting essentially of, and comprisedsubstantially of.

The phrases “in one embodiment,” “in some embodiments,” “according toone embodiment,” and the like generally mean that the particularfeature, structure, or characteristic following the phrase may beincluded in at least one embodiment of the present disclosure and may beincluded in more than one embodiment of the present disclosure(importantly, such phrases do not necessarily refer to the sameembodiment).

The word “example” is used herein to mean “serving as an example,instance, or illustration.” Any implementation described herein as“example” is not necessarily to be construed as preferred oradvantageous over other implementations.

If the specification states a component or feature “may,” “can,”“could,” “should,” “would,” “preferably,” “possibly,” “typically,”“optionally,” “for example,” “often,” or “might” (or other suchlanguage) be included or have a characteristic, that particularcomponent or feature is not required to be included or to have thecharacteristic. Such component or feature may be optionally included insome embodiments, or it may be excluded.

The terms “processor” and “processing circuitry” are used herein torefer to any programmable microprocessor, microcomputer or multipleprocessor chip or chips that can be configured by software instructions(applications) to perform a variety of functions, including thefunctions of the various embodiments described above. In some devices,multiple processors may be provided, such as one processor dedicated towireless communication functions and one processor dedicated to runningother applications. Software applications may be stored in the internalmemory before they are accessed and loaded into the processors. Theprocessors may include internal memory sufficient to store theapplication software instructions. In many devices the internal memorymay be a volatile or nonvolatile memory, such as flash memory, or amixture of both. The memory may also be located internal to anothercomputing resource (e.g., enabling computer readable instructions to bedownloaded over the Internet or another wired or wireless connection).

For the purposes of this description, a general reference to “memory”refers to memory accessible by the processors including internal memoryor removable memory plugged into the device, remote memory (e.g., cloudstorage), and/or memory within the processors themselves. For instance,memory may be any non-transitory computer readable medium havingcomputer readable instructions (e.g., computer program instructions)stored thereof that are executable by a processor.

The term “computing device” is used herein to refer to any one or all ofprogrammable logic controllers (PLCs), programmable automationcontrollers (PACs), industrial computers, desktop computers, personaldata assistants (PDAs), laptop computers, tablet computers, smart books,palm-top computers, personal computers, smartphone, headset, smartwatch,and similar electronic devices equipped with at least a processorconfigured to perform the various operations described herein. Devicessuch as smartphones, laptop computers, tablet computers, headsets, andsmartwatches are generally collectively referred to as mobile devices.

The term “server” or “server device” is used to refer to any computingdevice capable of functioning as a server, such as a master exchangeserver, web server, mail server, document server, or any other type ofserver. A server may be a dedicated computing device or a computingdevice including a server module (e.g., an application which may causethe computing device to operate as a server). A server module (e.g.,server application) may be a full function server module, or a light orsecondary server module (e.g., light or secondary server application)that is configured to provide synchronization services among the dynamicdatabases on computing devices. A light server or secondary server maybe a slimmed-down version of server type functionality that can beimplemented on a computing device, such as a smart phone, therebyenabling it to function as an Internet server (e.g., an enterprisee-mail server) only to the extent necessary to provide the functionalitydescribed herein.

The term “quantum basis” refers to sets of orthogonal quantum states,including, but not limited to, pairs of photonic polarization states.The pairs of photonic polarization states may comprise, for example, therectilinear, diagonal, and circular photonic polarization states. The“rectilinear basis” refers to the pair of rectilinear photonicpolarization states comprising the horizontal photon polarization state|0> and the vertical photon polarization state |1>. The “diagonal basis”refers to the pair of diagonal photonic polarization states comprisingthe diagonal photon polarization state of 45 degrees and the diagonalphoton polarization state 135 degrees. The “circular basis” refers tothe pair of circular photonic polarization states comprising the leftcircular photon polarization state |L> and the right circular photonpolarization state |R>.

The term “quantum particle” refers to photons, atoms, electrons,molecules, ions, or other suitable particles or quasi-particles (e.g.,composite fermions). The term “entangled quantum particle” refers to twoor more photons, atoms, electrons, molecules, ions, or other suitableparticles or quasi-particles entangled according to the principles ofquantum entanglement.

The term “qubit” refers to a basic unit of quantum informationcomprising a two-state, or two-level, quantum mechanical system, suchas: the polarization of a single photon (e.g., a photon encoded using aquantum basis as previously defined); the spin of a single electron(e.g., a spin qubit comprising the spin up state |1> and the spin downstate |0>); the energy level of a single atom (e.g., a superconductingqubit); the Hall conductance of electron systems (e.g., qubits based ona quantum Hall effect, such as an integer quantum Hall effect, afractional quantum Hall effect, or a quantum spin Hall effect); thevibration state of a single carbon nanotube or nanoparticle (e.g., acarbon qubit, a carbon nanotube or nanoparticle coupled to a spin qubit,a carbon nanotube or nanoparticle coupled to a superconducting qubit);the electronic state of an ion (e.g., a trapped ion); a transmissionline shunted plasma oscillation qubit (e.g., a fixed-frequency transmonqubit, a frequency-tunable transmon qubit); a charge qubit (e.g., asuperconducting charge qubit); a defect (e.g., a vacancy, a dopant, or acombination thereof, such as a nitrogen-vacancy center or asilicon-vacancy center) in a diamond structure (e.g., a diamond qubit);or any other suitable qubit. Qubits may exist in multiple statessimultaneously and can be made of any suitable quantum particle,including entangled quantum particles. Qubits may exist in multiplestates simultaneously and may be made of quantum particles such asphotons, atoms, electrons, molecules, ions, or other suitable particles,such as quasi-particles. In some embodiments, qubits may be entangledaccording to the principles of quantum entanglement. For example, a pairof entangled qubits may comprise a first entangled qubit and a secondentangled qubit, where measurement of the first entangled qubit causesthe collapse of the second entangled qubit such that the first entangledqubit and the second entangled qubit are equal (e.g., both “0” or both“1”) when measured using the same quantum basis.

The term “optical line” refers to an optical communications path. Forexample, an optical line may comprise an optical fiber, an opticalwaveguide, a fiberoptic cable, a non-polarization maintaining opticalfiber, an optical transmission line, a quantum line, or a combinationthereof. The term optical line broadly encompasses on-chip opticallines.

The term “quantum line” refers to a quantum communications path. Forexample, a quantum line may comprise a polarization-maintaining (PM)optical fiber (PMF or PM fiber), photonic transmission lines, photoniccrystals, photonic circuitry, free space (e.g., air, vacuum), or acombination thereof. In some embodiments, a PM fiber uses birefringenceto maintain the polarization states of photons. This is normally done bycausing consistent asymmetries in the PM fiber. Example PM fiber typesinclude: panda fiber which is used in telecom; elliptical clad fiber;and bowtie fiber. Any of these three designs uses birefringence byadding asymmetries to the fiber through shapes and stresses introducedin the fiber. This causes two polarization states to have differentphase velocities in the fiber. As such, an exchange of the overallenergy of the two modes (polarization states) becomes practicallyimpossible. The term optical line broadly encompasses on-chip quantumlines.

The term “on-chip encoder” and “on-chip decoder” is used herein to referto any device that respectively encodes or decodes a qubit ofinformation, or in time-bins of information, on a photon or an electron.In this regard, the qubit decoder may comprise an optoelectronic deviceas described below.

The terms “optoelectronic device,” “optoelectronic component,” “laserdevice,” “light source,” “single photon source,” “particle source,” andsimilar terms are used herein interchangeably to refer to any one ormore of (including, but not limited to, combinations of): a polarizedlight modulator (PLM); a polarized light demodulator (PLD); aquantization circuit; a laser device, such as a diode laser, a verticalcavity surface emitting laser (VCSEL), a semiconductor laser, afiberoptic laser, or an edge-emitting laser (e.g., a gallium arsenide(GaAs) edge-emitting laser comprising an indium gallium arsenide(InGaAs) quantum well); a light source; a single photon source; amodulator or modulating circuit; a photodetector device, such as aphotodetector, an array of photodetectors, or a photodetector panel; alight emitting device, such as a light emitting diode (LED), an array ofLEDs, an LED panel, or an LED display; a sensing device, such as one ormore sensors; any other device equipped with at least one of thematerials, structures, or layers described herein; an optical component,such as an optical lens, attenuator, deflector, phase shifter, filter,mirror, window, diffuser, prism, lenses, crystals (e.g., non-linearcrystals), wave plates, beam splitter, bit manipulator, polarizer, ordiffraction grating; an interferometer implemented as a Mach-Zehnderinterferometer (MZI), Fabry-Perot interferometer, Michelsoninterferometer, any other suitable configuration, or any combination orpermutation thereof; any device configured to function as any of theforegoing devices; or any combination thereof. In some embodiments, thelaser device may use a VCSEL to generate photons, qubits (e.g., bymodulating photons), or both. In some embodiments, a polarization pulseshaper may be integrated with the laser chip on the same laser device.In some embodiments, modulating circuitry (e.g., a modulating circuit)may be implemented on a board. Examples of a laser device may comprise afiberoptic laser with a polarizing component, an edge-emitting laser, aVCSEL, a PLM, or any other suitable device. In some embodiments, thelaser may generate photons, qubits, or both in the infrared ornear-infrared range (e.g., 1550 nanometers (nm), 980 nm, 900 nm). Forexample, a laser device may be an edge-emitting laser chip having afootprint smaller than one square millimeter and a thickness less than afew micrometers (microns) and comprising a gallium arsenide (GaAs)-basededge-emitting laser, a modulating circuit, and an attenuator ordeflector. Each of the MZIs disclosed herein may comprise a combinationof mirrors, beam splitters, photodetectors fiberoptic cables, lenses,nonlinear crystals, wave plates, motors (e.g., servo motors), motioncontrollers (e.g., servo motor controllers), temperature controllers(e.g., thermoelectric devices), and any other suitable componentsarranged to perform the operations and functions disclosed herein,including, but not limited to, the controlling of optical path length.In some embodiments, a first optoelectronic device may include aparticle source configured to generate single particles (e.g., photonsor electrons) and transmit the generated particles through a double-slitstructure to a first electron detector (e.g., “|1>”) and a secondelectron detector (e.g., “|0>”) as described herein.

The term “run-time hotspot” refers to a portion of code (i.e., programinstructions that, when executed, cause a system to perform certainfunctions) previously executed, being executed, or to be executed.

The term “real-time purchase data” refers to data representing areal-time purchase of assets. Real-time purchase data may be initiallygenerated at a client device then later transmitted to a QC system.

The term “real-time sell data” refers to data representing a real-timesell of a assets. Real-time sell data may be generated at a clientdevice then later transmitted to a QC system.

The term “collateral expiration data” refers to data representingexpiration and/or expiration dates of assets that expire, such asperiodic expiration dates and/or triple witching dates. Collateralexpiration data may be generated at a client device then latertransmitted to a QC system.

The term “optimization factor data” refers to data representing factorsthat may be used for optimizing (which may be referred to asoptimization factors) an asset, a portfolio, or a company such as:market data representing any factors that may impact markets, stock datarepresenting any factors that may impact stocks, bond data representingany factors that may impact bonds, currency data representing anyfactors that may impact currencies and/or foreign exchange rates,options data representing any factors that may impact options (e.g.,expiration dates, volatility, etc.), futures data representing anyfactors that may impact futures, commodity data representing any factorsthat may impact commodities, exogenous data, world economy datarepresenting any factors that may impact world economy (e.g.,populations, census, natural disasters, government actions, legislation,international events, etc.), life event data representing any personallife event associated with a customer associated with the portfolio(e.g., graduating school, obtaining a job, getting married, havingchildren, caring for others, and/or retirement, etc.), personalportfolio factor data representing customer-defined personal preferenceon managing a portfolio (e.g., maximize growth, minimize volatility,attain a specified value, keep a specified value of cash in theportfolio, minimize risk, etc.), personal portfolio constraints datarepresenting customer-defined constraints (e.g., owning assets or notowning assets associated with a specified location, owning assets or notowning assets at certain times, owning assets or not owning assetsassociated with a certain industries (e.g., sin stocks), owning assetsor not owning assets related to specified products, and/or owning assetsor not owning assets related to entities, organizations, and/orbusinesses, etc.), or the like. A QC system may filter optimizationfactor data to generate filtered portfolio optimization factor data anduse the filtered portfolio optimization factor data to optimize adetermination regarding the portfolio, such as an optimizationdetermination, efficient frontier determination, tracking errordetermination, and/or stress testing determination. The QC system mayalso use one or more sets of optimization factor data or filteredportfolio optimization factor data in testing simulations of portfolio,such as CVAR (conditional value at risk) or CCAR (comprehensive capitalanalysis and review) testing simulations.

The term “testing scenario” refers to scenarios that may be used forstress testing simulations and/or forecasts for different factors.Testing scenarios may include specific types of tests, such as CVARtesting or CCAR testing. The testing of a testing scenario may beperformed by the QC system, which may include optimizing a determinationfor optimization factor data related to the testing scenario. In someembodiments, the QC system may perform testing simulations iterativelyor simultaneously on a periodic or an on-demand basis using one or moreQC algorithms.

Having set forth a series of definitions called-upon throughout thisapplication, an example system architecture is described below forimplementing example embodiments and features of the present disclosure.

System Architecture

Methods, systems, apparatuses, and computer program products of thepresent disclosure may be embodied by any of a variety of devices. Forexample, the method, system, apparatus, and computer program product ofan example embodiment may be embodied by one or more networked devices,such as one or more servers, remote servers, cloud-based servers (e.g.,cloud utilities), or other network entities, and configured tocommunicate with one or more devices, such as one or more serverdevices, client devices, database server devices, remote server devices,other suitable devices, or a combination thereof.

In some instances, the method, system, apparatus, and computer programproduct of an example embodiment may be embodied by one or more quantumcommunications circuitries, such as one or more quantum particleencoders, quantum particle decoders, laser devices, quantum lines,quantum particle storage devices, other suitable quantum communicationsdevices or components, or a combination thereof.

Example embodiments of the client devices include any of a variety ofstationary or mobile computing devices, such as a mobile telephone,smartphone, smartwatch, smart speaker, portable digital assistant (PDA),tablet computer, laptop computer, desktop computer, kiosk computer,automated teller machine (ATM), point of sale (PoS) device, electronicworkstation, any other suitable computing device, or any combination ofthe aforementioned devices.

FIG. 1 illustrates a system diagram of a set of devices that may beinvolved in some example embodiments described herein. In this regard,FIG. 1 discloses an example environment 100 within which embodiments ofthe present disclosure may operate to provide portfolio optimization. Asillustrated, a QC system 102 may be connected to one or more QC serverdevices 104 in communication with one or more QC databases 106. The QCsystem 102 may be connected to one or more server devices 110A-110N, oneor more client devices 112A-112N, one or more database server devices114, and one or more remote server devices 116 through one or morecommunications networks 108. One or more communications networks 108 mayinclude any suitable network or combination of networks, such as avirtual network, the Internet, a local area network (LAN), a Wi-Finetwork, a Worldwide Interoperability for Microwave Access (WiMAX)network, a home network, a cellular network, a near field communications(NFC) network, other types of networks, or a combination thereof. Insome embodiments, the QC system 102 may be configured to provideportfolio optimization as described in further detail below.

The QC system 102 may be embodied as one or more specializedcircuitries, computers, or computing systems and may comprise one ormore QC server devices 104 and one or more QC databases 106. The one ormore QC server devices 104 may be embodied as one or more servers,remote servers, cloud-based servers (e.g., cloud utilities), processors,any other suitable server devices, or any combination thereof. The oneor more QC server devices 104 may be configured to receive, process,generate, and transmit data, signals, and electronic information tofacilitate the operations of the QC system 102. The one or more QCdatabases 106 may be embodied as one or more data storage devices, suchas Network Attached Storage (NAS) devices or separate databases orservers. The one or more QC databases 106 may be configured to store andprovide access to data and information used by the QC system 102 tofacilitate the operations of the QC system 102. For example, the one ormore QC databases 106 may store user account credentials for users ofone or more server devices 110A-110N, one or more client devices112A-112N, one or more database server devices 114, one or more remoteserver devices 116, or a combination thereof. In another example, theone or more QC databases 106 may store data regarding devicecharacteristics for the one or more server devices 110A-110N, one ormore client devices 112A-112N, one or more database server devices 114,one or more remote server devices 116, or a combination thereof. In someembodiments, the one or more QC server devices 104, the one or more QCdatabases 106, or both may include or store various data and electronicinformation associated with one or more data, data attributes, dataenvelopes, enveloped data structures, portfolio data, collateral data,risk level data, time data, policy information, real-time purchase data,real-time sell data, collateral expiration data, optimization factordata, life event data, personal portfolio factor data, machine learningmodel, non-QC algorithms, non-QC algorithm performance information, QCalgorithm performance information, QC algorithms, other machine learningtechniques, graphical user interface (GUI) data, any other suitable dataor electronic information, any links or pointers thereto, orcombinations thereof. In some embodiments, the one or more QC serverdevices 104, the one or more QC databases 106, or both may include orstore various quantum information, such as one or more quantum particles(e.g., pairs of entangled quantum particles, one entangled quantumparticle in a pair of entangled quantum particles), quantumcryptographic keys, quantum one-time pads, any other suitable quantuminformation, any links or pointers thereto, or combinations thereof.

The one or more server devices 110A-110N may be embodied by one or morecomputing devices. In some embodiments, the one or more server devices110A-110N may be embodied as one or more servers, remote servers,cloud-based servers (e.g., cloud utilities), processors, or any othersuitable devices, or any combination thereof. In some embodiments, theone or more server devices 110A-110N may receive, process, generate, andtransmit data, signals, and electronic information to facilitate theoperations of the QC system 102. Information received by the QC system102 from one or more server devices 110A-110N may be provided in variousforms and via various methods. In some embodiments, the one or moreserver devices 110A-110N may include or store various data andelectronic information associated with one or more data, dataattributes, data envelopes, enveloped data structures, asset data,portfolio data, collateral data, risk level data, time data, policyinformation, real-time purchase data, real-time sale data, life eventdata, personal portfolio factor data, optimization factor data, stresstesting factor data, stress testing scenario data, and/or hurricanescenario data, collateral expiration data, life event data, personalportfolio factor data, personal portfolio constraints data, machinelearning techniques, GUI data, any other suitable data or electronicinformation, any links or pointers thereto, or combinations thereof. Insome embodiments, the one or more server devices 110A-110N may includeor store various quantum information, such as one or more quantumparticles (e.g., pairs of entangled quantum particles, one entangledquantum particle in a pair of entangled quantum particles), quantumcryptographic keys, quantum one-time pads, any other suitable quantuminformation, any links or pointers thereto, or combinations thereof.

The one or more client devices 112A-112N may be embodied by one or morecomputing devices. Information received by the QC system 102 from theone or more client devices 112A-112N may be provided in various formsand via various methods. For example, the one or more client devices112A-112N may be smartphones, laptop computers, netbooks, tabletcomputers, wearable devices, desktop computers, ATMs, PoS devices,electronic workstations, or the like, and the information may beprovided through various modes of data transmission provided by theseclient devices. In some embodiments, the one or more client devices112A-112N may include or store various data and electronic informationassociated with one or more users. For example, the one or more clientdevices 112A-112N may include or store user information (including, butnot limited to, user profile information), any other suitable data, orany combination thereof. In some embodiments, the one or more clientdevices 112A-112N may include or store various data and electronicinformation associated with one or more data, data attributes, dataenvelopes, enveloped data structures, portfolio data, collateral data,risk level data, time data, policy information, real-time purchase data,real-time sale data, life event data, personal portfolio factor data,optimization factor data, stress testing factor data, stress testingscenario data, and/or hurricane scenario data, collateral expirationdata, life event data, personal portfolio factor data, personalportfolio constraints data, machine learning techniques, GUI data, anyother suitable data or electronic information, any links or pointersthereto, or combinations thereof. In some embodiments, the one or moreclient devices 112A-112N may include or store various quantuminformation, such as one or more quantum particles (e.g., pairs ofentangled quantum particles, one entangled quantum particle in a pair ofentangled quantum particles), quantum cryptographic keys, quantumone-time pads, any other suitable quantum information, any links orpointers thereto, or combinations thereof.

In embodiments where a client device 112 is a mobile device, such as asmartphone or tablet, the mobile device may execute an “app” (e.g., athin-client application) to interact with the QC system 102, one or moreserver devices 110A-110N, one or more database server devices 114, oneor more remote server devices 116, or a combination thereof. Such appsare typically designed to execute on mobile devices, such as tablets orsmartphones. For example, an app may be provided that executes on mobiledevice operating systems such as Apple Inc.'s iOS, Google LLC'sAndroid®, or Microsoft Corporation's Windows®. These platforms typicallyprovide frameworks that allow apps to communicate with one another andwith particular hardware and software components of mobile devices. Forexample, the mobile operating systems named above each provideframeworks for interacting with camera circuitry, microphone circuitry,sensor circuitry, location services circuitry, wired and wirelessnetwork interfaces, user contacts, and other applications in a mannerthat allows for improved interactions between apps while also preservingthe privacy and security of individual users. In some embodiments, amobile operating system may also provide for improved communicationinterfaces for interacting with external devices (e.g., server devices,client devices, database server devices, remote server devices).Communication with hardware and software modules executing outside ofthe app is typically provided via APIs provided by the mobile deviceoperating system.

The one or more database server devices 114 may be embodied by one ormore computing devices, server devices, servers, data storage devices,databases, or a combination thereof. In some embodiments, the one ormore database server devices 114 may be embodied as one or more datastorage devices, such as one or more NAS devices, or as one or moreseparate databases or database servers. In some embodiments, the one ormore database server devices 114 may be embodied as one or more servers,remote servers, cloud-based servers (e.g., cloud utilities), processors,or any other suitable devices, or any combination thereof. In someembodiments, the one or more database server devices 114 may receive,process, generate, and transmit data, signals, and electronicinformation to facilitate the operations of the QC system 102.Information received by the QC system 102 from one or more databaseserver devices 114 may be provided in various forms and via variousmethods. It will be understood, however, that in some embodiments, theone or more database server devices 114 need not themselves be databasesor database servers but may be peripheral devices communicativelycoupled to databases or database servers.

In some embodiments, the one or more database server devices 114 mayinclude or store various data and electronic information associated withone or more data, data attributes, data envelopes, enveloped datastructures, portfolio data, collateral data, risk level data, time data,policy information, real-time purchase data, real-time sale data, lifeevent data, personal portfolio factor data, optimization factor data,stress testing factor data, stress testing scenario data, and/orhurricane scenario data, machine learning techniques, GUI data, anyother suitable data or electronic information, any links or pointersthereto, or combinations thereof. In some embodiments, the one or moredatabase server devices 114 may include or store exogenous data. Theexogenous data may comprise, for example, public sentiment datastructures (e.g., a widespread data breach at a third-party system, suchas a merchant; a stock market crash; a geopolitical event), newsarticles, FDIC data, NIST data, company intranet data, technologicaladvancements, scientific publications, financial data (e.g., stockmarket data, commodity market data, money market data), legal data(e.g., lawsuit data, regulatory data), any other suitable exogenousdata, or any combination thereof. In some embodiments, the one or moredatabase server devices 114 may include or store various quantuminformation, such as one or more quantum particles (e.g., pairs ofentangled quantum particles, one entangled quantum particle in a pair ofentangled quantum particles), quantum cryptographic keys, quantumone-time pads, any other suitable quantum information, any links orpointers thereto, or combinations thereof.

The one or more remote server devices 116 may be embodied by one or morecomputing devices, server devices, servers, data storage devices,databases, or a combination thereof. In some embodiments, the one ormore remote server devices 116 may be embodied as one or more datastorage devices, such as one or more NAS devices, or as one or moreseparate databases or database servers. In some embodiments, the one ormore remote server devices 116 may be embodied as one or more servers,remote servers, cloud-based servers (e.g., cloud utilities), processors,or any other suitable devices, or any combination thereof. In someembodiments, the one or more remote server devices 116 may receive,process, generate, and transmit data, signals, and electronicinformation to facilitate the operations of the QC system 102.Information received by the QC system 102 from one or more remote serverdevices 116 may be provided in various forms and via various methods. Itwill be understood, however, that in some embodiments, the one or moreremote server devices 116 need not themselves be servers but may beperipheral devices communicatively coupled to servers.

In some embodiments, the one or more remote server devices 116 mayinclude or store various data and electronic information associated withone or more data, data attributes, data envelopes, enveloped datastructures, portfolio data, collateral data, risk level data, time data,policy information, real-time purchase data, real-time purchase data,real-time sale data, life event data, personal portfolio factor data,optimization factor data, stress testing factor data, stress testingscenario data, and/or hurricane scenario data, machine learningtechniques, GUI data, exogenous data, any other suitable data orelectronic information, any links or pointers thereto, or combinationsthereof. In some embodiments, the one or more remote server devices 116may include or store various quantum information, such as one or morequantum particles (e.g., pairs of entangled quantum particles, oneentangled quantum particle in a pair of entangled quantum particles),quantum cryptographic keys, quantum one-time pads, any other suitablequantum information, any links or pointers thereto, or combinationsthereof.

In some embodiments, the one or more server devices 110A-110N, the oneor more client devices 112A-112N, the one or more database serverdevices 114, the one or more remote server devices 116, or anycombination thereof may interact with the QC system 102 over one or morecommunications networks 108. As yet another example, the one or moreserver devices 110A-110N, the one or more client devices 112A-112N, theone or more database server devices 114, the one or more remote serverdevices 116, or a combination thereof may include various hardware orfirmware designed to interface with the QC system 102. For example, anexample server device 110A may be a session authentication servermodified to communicate with the QC system 102, and another exampleserver device 110B may be a purpose-built session authentication serveroffered for the primary purpose of communicating with the QC system 102.As another example, an example client device 112A may be a user'ssmartphone and may have an application stored thereon facilitatingcommunication with the QC system 102, whereas another example clientdevice 112B may be a purpose-built device offered for the primarypurpose of communicating with the QC system 102.

In some embodiments, the one or more server devices 110A-110N, the oneor more client devices 112A-112N, the one or more database serverdevices 114, the one or more remote server devices 116, or anycombination thereof may interact with the QC system 102 over one or morePQC communications channels. The PQC communications channel may be, forexample, a communications channel over which data is transmitted andreceived using a PQC cryptographic technique, such as a PQC back channel(e.g., a PQC out-of-band communications channel).

As a foundation for some embodiments, the QC system 102 may provide forreceiving data and generating a set of data attributes about the data.In some embodiments, the QC system 102 may provide for receiving,directly or indirectly via communications network 108, the data from oneor more of the one or more client devices 112A-112N, the one or moreserver devices 110A-110N, the one or more database server devices 114,any other suitable device, or any combination thereof. In someembodiments, the QC system 102 may further provide for generating a dataenvelope based on the set of data attributes. In some embodiments, theQC system 102 may further provide for generating an enveloped datastructure based on the data envelope and the data.

In some embodiments, the QC system may communicate with one or more ofthe one or more client devices 112A-112N, the one or more server devices110A-110N, the one or more database server devices 114, the one or moreremote server devices 116, any other suitable device, or any combinationthereof.

In some embodiments, the QC system 102 may further provide forgenerating a portfolio view (e.g., GUI) for enabling customer input oflife event data representing any personal life event associated with acustomer associated with the portfolio, personal portfolio factor datarepresenting customer-defined personal preference on managing aportfolio, personal portfolio constraints data representingcustomer-defined constraints, or the like.

Example Implementing Apparatuses

The QC system 102 described with reference to FIG. 1 may be embodied byone or more computing systems, such as apparatus 200 shown in FIG. 2 .In some embodiments, apparatus 200 shown in FIG. 2 may represent anexample QC system 102, a QC server device 104, a QC database 106, or acombination thereof.

As illustrated in FIG. 2 , the apparatus 200 may include one or more ofprocessing circuitry 202, memory 204, input-output circuitry 206,communications circuitry 208 (including, but not limited to, classicalcommunications circuitry 210 and quantum communications circuitry 212),data attribute generation circuitry 214, data envelope generationcircuitry 216, code testing circuitry 218, data monitoring circuitry 220(including, but not limited to, data access monitoring circuitry 222 anddata zone monitoring circuitry 224), optimization factor filteringcircuitry 226, QC optimization circuitry 228, code identificationcircuitry 230, algorithm performance circuitry 232, algorithm selectioncircuitry 234, machine learning circuitry 252, code performanceevaluation circuitry 254, data storage circuitry 256, user interface(UI) circuitry 258, any other suitable circuitry, or any combinationthereof. The apparatus 200 may be configured to execute the operationsdescribed above with respect to FIG. 1 and below with respect to FIGS. 3and 5-7 .

In some embodiments, the processing circuitry 202 (and/or co-processoror any other processing circuitry assisting or otherwise associated withthe processor) may be in communication with the memory 204 via a bus forpassing information among components of the apparatus 200. The memory204 may be non-transitory and may include, for example, one or morevolatile and/or non-volatile memories. For example, the memory may be anelectronic storage device (e.g., a computer readable storage medium).The memory 204 may be configured to store information, data, datastructures, content, control signals, applications, instructions, or thelike, for enabling the apparatus to carry out various functions inaccordance with example embodiments of the present disclosure. In someinstances, the memory 204 may be configured to store data, datastructures, data elements, and electronic information associated withone or more data, data attributes, data envelopes, enveloped datastructures, portfolio data, collateral data, risk level data, time data,policy information, real-time purchase data, real-time sale data, lifeevent data, personal portfolio factor data, optimization factor data,stress testing factor data, stress testing scenario data, and/orhurricane scenario data, machine learning model, non-QC algorithms,non-QC algorithm performance information, QC algorithm performanceinformation, QC algorithms, other machine learning techniques, graphicaluser interface (GUI) data, any other suitable data or electronicinformation, or combinations thereof. It will be understood that thememory 204 may be configured to store any data, data structures,electronic information, requests, embodiments, examples, figures,techniques, processes, operations, methods, systems, apparatuses, orcomputer program products described herein, or any combination thereof.

The processing circuitry 202 may be embodied in a number of differentways and may, for example, include one or more processing devicesconfigured to perform independently. Additionally, or alternatively, theprocessing circuitry 202 may include one or more processors configuredin tandem via a bus to enable independent execution of instructions,pipelining, multithreading, or a combination thereof. The use of theterm “processing circuitry” may be understood to include a single coreprocessor, a multi-core processor, multiple processors internal to theapparatus, remote or “cloud” processors, or a combination thereof.

In an example embodiment, the processing circuitry 202 may be configuredto execute instructions stored in the memory 204 or otherwise accessibleto the processor. Alternatively, or additionally, the processingcircuitry 202 may be configured to execute hard-coded functionality. Assuch, whether configured by hardware or software methods, or by acombination of hardware with software, the processor may represent anentity (e.g., physically embodied in circuitry) capable of performingoperations according to an embodiment of the present disclosure whileconfigured accordingly. As another example, when the processor isembodied as an executor of software instructions, the instructions mayspecifically configure the processor to perform the functionalities andoperations described herein when the instructions are executed.

In some embodiments, the apparatus 200 may include input-outputcircuitry 206 that may, in turn, be in communication with processingcircuitry 202 to provide output to the user and, in some embodiments, toreceive an indication of a user input such as a command provided by auser. The input-output circuitry 206 may comprise a user interface(e.g., a user interface generated by user interface circuitry includedin the apparatus 200) comprising a display that may include a web userinterface, a mobile application, a client device, a display device, adisplay screen, or any other suitable hardware or software. In someembodiments, the input-output circuitry 206 may also include a keyboard,a mouse, a joystick, a touch screen, touch areas, soft keys, amicrophone, a speaker, or other input-output mechanisms. The processingcircuitry 202, the input-output circuitry 206 (which may utilize theprocessing circuitry 202), or both may be configured to control one ormore functions of one or more user interface elements through computerprogram instructions (e.g., software, firmware) stored on a memory(e.g., memory 204). Input-output circuitry 206 is optional and, in someembodiments, the apparatus 200 may not include input-output circuitry.For example, where the apparatus 200 does not interact directly with theuser, the apparatus 200 may be configured to generate (e.g., by UIcircuitry 258) user interface data (e.g., data attribute GUI data, riskprofile GUI data, PQC optimization GUI data, data monitoring GUI data)for display by one or more other devices with which one or more usersdirectly interact and transmit the generated user interface data to oneor more of those devices.

The communications circuitry 208 may be any device or circuitry embodiedin either hardware or a combination of hardware and software that isconfigured to receive and/or transmit classical data, quantuminformation, or both from or to a network and/or any other device,circuitry, or module in communication with the apparatus 200. In thisregard, the communications circuitry 208 may include, for example,classical communications circuitry 210 and quantum communicationscircuitry 212.

The classical communications circuitry 210 may be any device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data from or to anetwork and/or any other device, circuitry, or module in communicationwith the apparatus 200. In this regard, the classical communicationscircuitry 210 may include, for example, a network interface for enablingcommunications with a wired or wireless communications network. Forexample, the classical communications circuitry 210 may include one ormore network interface cards, antennae, buses, switches, routers,modems, and supporting hardware and/or software, or any other devicesuitable for enabling communications via a network. In some embodiments,the communication interface may include the circuitry for interactingwith the antenna(s) to cause transmission of signals via the antenna(s)or to handle receipt of signals received via the antenna(s). Thesesignals may be transmitted by the apparatus 200 using any of a number ofwireless personal area network (PAN) technologies, such as Bluetooth®v1.0 through v5.0, Bluetooth Low Energy (BLE), infrared wireless (e.g.,IrDA), ultra-wideband (UWB), induction wireless transmission, or anyother suitable technologies. In addition, it should be understood thatthese signals may be transmitted using Wi-Fi, NFC, WiMAX or otherproximity-based communications protocols.

The quantum communications circuitry 212 may be any device or circuitryembodied in either hardware or a combination of hardware and softwarethat is configured to receive and/or transmit quantum particles, such asphotons, electrons, or both from or to any other device, circuitry, ormodule in communication with the apparatus 200. In this regard, thequantum communications circuitry 212 may include, for example, opticalcomponents such as an optical communications interface for enablingoptical communications over a quantum line. In some embodiments, thequantum communications circuitry 212 may include encoding circuitry(e.g. an on-chip encoder) to generate a set of entangled quantumparticles (e.g., qubits, qutrits, qudits) and decoding circuitry (e.g.,an on-chip decoder) to receive (e.g., directly or indirectly, such asvia switching circuitry), store, and measure a set of entangled quantumparticles. In some embodiments, the quantum communications circuitry 212may further include quantum basis determination circuitry configured todetermine the quantum bases, or sets of quantum bases, for encoding anddecoding of a given set of quantum particles. In some embodiments, thequantum communications circuitry 212 may include or be communicativelycoupled to one or more quantum storage devices configured to storevarious quantum information, such as one or more quantum particles(e.g., pairs of entangled quantum particles, one entangled quantumparticle in a pair of entangled quantum particles), quantumcryptographic keys, quantum one-time pads, any other suitable quantuminformation, any links or pointers thereto, and combinations thereof.

In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain data. In some embodiments, the data maycomprise data access control information, a link or pointer to the data(e.g., a link to a credit card number), a bitstream, a binary largeobject (BLOB), any other suitable data, or any combination thereof. Insome embodiments, the data may have been encrypted based on a set ofencryption attributes, such as a set of non-PQC encryption attributes, aset of PQC encryption attributes, or both (e.g., double encryption wherethe data has been encrypted based on a set of non-PQC encryptionattributes and then double encrypted based on a set of PQC encryptionattributes). In some embodiments, the communications circuitry 208 maybe configured to receive, retrieve, or obtain the data from a datastorage device, such as memory 204, one or more of the one or more QCdatabases 106, the one or more database server devices 114 (including,but not limited to, one or more data storage devices communicativelycoupled, either directly or indirectly, to the one or more databaseserver devices 114), the one or more remote server devices 116, the oneor more server devices 110A-110N, the one or more client devices112A-112N, any other suitable device or circuitry, or a combinationthereof.

In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain a set of data attributes about the data.In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain the set of data attributes from anothercircuitry, such as processing circuitry 202, data attribute generationcircuitry 214, any other suitable circuitry, or a combination thereof.In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain the set of data attributes from a datastorage device, such as memory 204, one or more of the one or more QCdatabases 106, the one or more database server devices 114 (including,but not limited to, one or more data storage devices communicativelycoupled, either directly or indirectly, to the one or more databaseserver devices 114), the one or more remote server devices 116, the oneor more server devices 110A-110N, the one or more client devices112A-112N, any other suitable device or circuitry, or a combinationthereof. In some embodiments, where the data is included in an envelopeddata structure comprising the data and a data envelope that comprisesthe set of data attributes, the communications circuitry 208 may beconfigured to receive, retrieve, or obtain the set of data attributes byextracting the set of data attributes from the data envelope.

In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain policy information associated with thedata. In some embodiments, the communications circuitry 208 may beconfigured to receive, retrieve, or obtain the policy information fromanother circuitry, such as processing circuitry 202, policy attributegeneration circuitry 226, any other suitable circuitry, or a combinationthereof. In some embodiments, the communications circuitry 208 may beconfigured to receive, retrieve, or obtain the policy information from adata storage device, such as memory 204, one or more of the one or moreQC databases 106, the one or more database server devices 114(including, but not limited to, one or more data storage devicescommunicatively coupled, either directly or indirectly, to the one ormore database server devices 114), the one or more remote server devices116, the one or more server devices 110A-110N, the one or more clientdevices 112A-112N, any other suitable device or circuitry, or acombination thereof.

In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain real-time purchase data, real-time saledata, life event data, personal portfolio factor data, optimizationfactor data, stress testing factor data, stress testing scenario data,and/or hurricane scenario data. In some embodiments, the communicationscircuitry 208 may be configured to receive, retrieve, or obtain thereal-time purchase data, real-time sale data, life event data, personalportfolio factor data, optimization factor data, stress testing factordata, stress testing scenario data, and/or hurricane scenario data, suchas processing circuitry 202, UI circuitry 258, any other suitablecircuitry, or a combination thereof. In some embodiments, thecommunications circuitry 208 may be configured to receive, retrieve, orobtain the real-time purchase data, real-time sale data, life eventdata, personal portfolio factor data, optimization factor data, stresstesting factor data, stress testing scenario data, and/or hurricanescenario data from a data storage device, such as memory 204, one ormore of the one or more QC databases 106, the one or more databaseserver devices 114 (including, but not limited to, one or more datastorage devices communicatively coupled, either directly or indirectly,to the one or more database server devices 114), the one or more remoteserver devices 116, the one or more server devices 110A-110N, the one ormore client devices 112A-112N, any other suitable device or circuitry,or a combination thereof. In some embodiments, where the data isincluded in an enveloped data structure comprising the data and a dataenvelope that comprises the real-time purchase data, real-time saledata, life event data, personal portfolio factor data, optimizationfactor data, stress testing factor data, stress testing scenario data,and/or hurricane scenario data, or life event data, the communicationscircuitry 208 may be configured to receive, retrieve, or obtain thereal-time purchase data, real-time sale data, life event data, orpersonal portfolio factor data by extracting the real-time purchasedata, real-time sale data, life event data, or personal portfolio factordata from the data envelope.

In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain optimization factor data. In someembodiments, the communications circuitry 208 may be configured toreceive, retrieve, or obtain the collateral expiration data andoptimization factor data from another circuitry, such as processingcircuitry 202, input-output circuitry 206, UI circuitry 258, any othersuitable circuitry, or a combination thereof. In some embodiments, thecommunications circuitry 208 may be configured to receive, retrieve, orobtain the collateral expiration data and optimization factor data froma data storage device, such as memory 204, one or more of the one ormore QC databases 106, the one or more database server devices 114(including, but not limited to, one or more data storage devicescommunicatively coupled, either directly or indirectly, to the one ormore database server devices 114), the one or more remote server devices116, the one or more server devices 110A-110N, the one or more clientdevices 112A-112N, any other suitable device or circuitry, or acombination thereof.

In some embodiments, the communications circuitry 208 may be configuredto receive, retrieve, or obtain QC algorithm performance informationassociated with a set of QC algorithms. In some embodiments, thecommunications circuitry 208 may be configured to receive, retrieve, orobtain the QC algorithm performance information from another circuitry,such as processing circuitry 202, algorithm performance circuitry 234,any other suitable circuitry, or a combination thereof. In someembodiments, the communications circuitry 208 may be configured toreceive, retrieve, or obtain the QC algorithm performance informationfrom a data storage device, such as memory 204, one or more of the oneor more QC databases 106, the one or more database server devices 114(including, but not limited to, one or more data storage devicescommunicatively coupled, either directly or indirectly, to the one ormore database server devices 114), the one or more remote server devices116, the one or more server devices 110A-110N, the one or more clientdevices 112A-112N, any other suitable device or circuitry, or acombination thereof.

The data attribute generation circuitry 214 includes hardware componentsdesigned or configured to request, receive, process, generate, andtransmit data, data structures, control signals, and electronicinformation for use in PQC. In some embodiments, the data attributegeneration circuitry 214 may be configured to generate a set of dataattributes about data, such as the data received by the communicationscircuitry 208, based on the data. In some embodiments, the dataattribute generation circuitry 214 may be configured to generate the setof data attributes about the data based on the data itself, overheaddata (e.g., protocol overhead, header, metadata) associated with thedata, any other suitable data or electronic information, or anycombination thereof. In some embodiments, the data attribute generationcircuitry 214 may be configured to generate the set of data attributesabout the data based on a machine learning technique, such as a machinelearning technique provided or performed by the code performanceevaluation circuitry 254.

In some embodiments, the set of data attributes about the data maycomprise a data lineage data attribute indicative of a data lineage ofthe data. For example, the data attribute generation circuitry 214 maybe configured to generate a data lineage data attribute indicative of adata lineage of the data, wherein the set of data attributes comprisesthe data lineage data attribute.

In some embodiments, the set of data attributes about the data maycomprise a cryptographic data attribute indicative of a cryptographictechnique used to encrypt the data. In some instances, the dataattribute generation circuitry 214 may be configured to generate,without user interactivity, the cryptographic data attribute based on anautomated analysis of a bitstream of the data. For example, the data maycomprise a bitstream, and the data attribute generation circuitry 214may be configured to generate, based on an automated analysis of thebitstream and without user interactivity, a cryptographic data attributeindicative of a cryptographic technique used to encrypt the data,wherein the set of data attributes comprises the cryptographic dataattribute.

In some embodiments, the set of data attributes about the data maycomprise a cryptographic spawn log indicative of a set of cryptographictechniques used to encrypt the data. For example, the data attributegeneration circuitry 214 may be configured to generate a cryptographicspawn log comprising a set of timestamps and information indicative ofsets of PQC encryption attributes used to encrypt the data over a periodof time (e.g., lifetime of the data; the last three years, or any othersuitable period or duration of time), wherein each timestamp in the setof timestamps corresponds to a set of PQC encryption attributes used toencrypt the data at the time associated with the timestamp. In anotherexample, the data attribute generation circuitry 214 may be configuredto generate a cryptographic spawn log comprising a set of encryptionidentification numbers and information indicative of sets of PQCencryption attributes used to encrypt the data over an amount ofencryptions (e.g., all encryptions; the last five encryptions, or anyother suitable amount of encryptions), wherein each encryptionidentification number in the set of encryption identification numberscorresponds to a set of PQC encryption attributes used to encrypt thedata at the time associated with the encryption identification number.In some embodiments, the data attribute generation circuitry 214 may beconfigured to generate a cryptographic spawn log comprising a timestampand information indicative of the set of PQC encryption attributes usedto encrypt the data. In some embodiments, the data attribute generationcircuitry 214 may be configured to generate an updated cryptographicspawn log comprising a second timestamp and information indicative of asecond set of PQC encryption attributes used to encrypt the data.

In some embodiments, the set of data attributes about the data maycomprise a data access log indicative of a set of data activitymonitoring information (e.g., database activity monitoring information,access credentials, user identification information, machineidentification information) associated with electronic access to thedata. For example, the data attribute generation circuitry 214 may beconfigured to generate a data access log comprising a set of timestampsand information indicative of sets of data activity monitoringinformation the data over a period of time (e.g., lifetime of the data;the last three years, or any other suitable period or duration of time),wherein each timestamp in the set of timestamps corresponds to a set ofPQC encryption attributes used to encrypt the data at the timeassociated with the timestamp. In some embodiments, the set of dataattributes about the data may comprise a determination that the data hasbeen accessed. For example, the data attribute generation circuitry 214may be configured to receive the determination that the data has beenaccessed from the data access monitoring circuitry 222.

In some embodiments, the set of data attributes about the data maycomprise a data zone data attribute indicative of a data zone associatedwith the data. For example, the data attribute generation circuitry 214may be configured to generate a data zone data attribute indicative of adata zone associated with the data, wherein the set of data attributescomprises the data zone data attribute. In some embodiments, the set ofdata attributes about the data may comprise a determination that thedata has transitioned from a first data zone to a second data zone. Forexample, the data attribute generation circuitry 214 may be configuredto receive the determination that the data has transitioned from a firstdata zone to a second data zone from the data zone monitoring circuitry224.

The data envelope generation circuitry 216 includes hardware componentsdesigned or configured to request, receive, process, generate, andtransmit data, data structures, control signals, and electronicinformation for use in PQC. In some embodiments, the data envelopegeneration circuitry 216 may be configured to generate a data envelopebased on the set of data attributes. In some embodiments, the dataenvelope generation circuitry 216 may be configured to generate the dataenvelope based on the set of data attributes. In some embodiments, thedata envelope may comprise the set of data attributes. In someembodiments, the data envelope generation circuitry 216 may beconfigured to generate the data envelope based on the set of dataattributes, a risk profile data structure, any other suitable data, orany combination thereof. In some embodiments, the data envelope maycomprise the set of data attributes, a risk profile data structure, anyother suitable data, or any combination thereof. In some embodiments,each piece of data may have a data envelope, wherein the data envelopecomprises one or more attributes about the data. In some embodiments,the data and its envelope may be referred to as a “data BLOB.” In someinstances, the data envelope will keep track of computing devices thataccessed the data, such as computing devices that took an encryptedsnapshot of the data and when that encrypted snapshot was taken.

The code testing circuitry 218 includes hardware components designed orconfigured to request, receive, process, generate, and transmit data,data structures, control signals, and electronic information. In anembodiment, the code testing circuitry 218 may be configured to runquantum optimization algorithms on one or more identified portions ofcode to improve the performance of the identified portions of code.

In another embodiment, the code testing circuitry 218 may be configuredto run testing of various QC algorithms to estimate, evaluate, and/ordetermine QC run costs in various scenarios, which may include whichfactors used in a QC algorithm may be static, varied, or omitted whenrunning a QC algorithm. In some embodiments, the identified portions ofcode may be related to QC based portfolio optimization, or otherapplicable applications. The quantum optimization algorithms may bealgorithms based on one or more of Quadratic Unconstrained BinaryOptimization “QUBO”, Quantum Approximate Optimization Algorithm “QAOA”,Quantum Machine Learning “QML”, Quantum Geometrodynamics “QGD”, QuantumMonte Carlo “QMC”, Harrow, Hassidim and Lloyd “HHL”, or the like. Insome embodiments, the code testing circuitry 218 may receive indicationsof the identified portions of code from the code identificationcircuitry 230.

The data monitoring circuitry 220 includes hardware components designedor configured to request, receive, process, generate, and transmit data,data structures, control signals, and electronic information for use inPQC. In some embodiments, the data monitoring circuitry 220 may beconfigured to monitor data, enveloped data structures, any othersuitable data or electronic information, or any combination thereof. Inthis regard, the data monitoring circuitry 220 may include, for example,data access monitoring circuitry 222 and data zone monitoring circuitry224.

In some embodiments, the data monitoring circuitry 220 may be configuredto monitor an enveloped data structure and identify changes in theenveloped data structure. In some embodiments, the enveloped datastructure may comprise a data envelope and data. In some embodiments,the data envelope may comprise a set of data attributes about the dataand a risk profile data structure indicative of a vulnerability of thedata in a PQC data environment. For example, the data monitoringcircuitry 220 may be configured to generate an electronic indication ofthe change in the enveloped data structure, such as a control signal,metadata, or flag indicative of the change. In some embodiments, thedata monitoring circuitry 220 may be configured to automatically monitorthe enveloped data structure in real-time and without userinteractivity; automatically identify the change in the enveloped datastructure in real-time and without user interactivity; and generate theelectronic indication of the change in the enveloped data structure inreal-time and without user interactivity.

The data access monitoring circuitry 222 includes hardware componentsdesigned or configured to request, receive, process, generate, andtransmit data, data structures, control signals, and electronicinformation for use in PQC. In some embodiments, the data accessmonitoring circuitry 222 may be configured to monitor the access ofdata, enveloped data structures, any other suitable data or electronicinformation, or any combination thereof. For example, the data accessmonitoring circuitry 222 may be configured to determine that the datahas been accessed, generate a determination that the data has beenaccessed, and transmit the determination that the data has been accessedto any suitable circuitry, such as the data attribute generationcircuitry 214.

In some embodiments, the data access monitoring circuitry 222 may beconfigured to generate a data access log indicative of a set of dataactivity monitoring information (e.g., database activity monitoringinformation, access credentials, user identification information,machine identification information) associated with electronic access tothe data. For example, the data access monitoring circuitry 222 may beconfigured to generate a data access log comprising a set of timestampsand information indicative of sets of data activity monitoringinformation the data over a period of time (e.g., lifetime of the data;the last three years, or any other suitable period or duration of time),wherein each timestamp in the set of timestamps corresponds to a set ofPQC encryption attributes used to encrypt the data at the timeassociated with the timestamp. In another example, the data accessmonitoring circuitry 222 may be configured to generate the set of dataactivity monitoring information and transmit the set of data activitymonitoring information to the data attribute generation circuitry 214,which may be configured to receive the set of data activity monitoringinformation and generate a data access log based on the set of dataactivity monitoring information.

In some embodiments, the optimization factor filtering circuitry 226includes hardware components designed or configured to request, receive,process, generate, and transmit data, data structures, control signals,and electronic information for use in QC based optimization, includingoptimizing an asset, a portfolio, or a company, such as an optimizationdetermination, an efficient frontier determination, a tracking errordetermination, and/or a stress testing determination. In someembodiments, the optimization factor filtering circuitry 226 may beconfigured to filter, based on pre-defined filtering criteria and/or oneor more machine learning techniques, optimization factor data togenerate filtered portfolio optimization factor data, filtered efficientfrontier factor data, filtered tracking error factor data, filteredstress testing factor data, and/or filtered hurricane factor data. Insome embodiments, the optimization factor filtering circuitry 226 mayretrieve optimization factor data from a data storage device, such asmemory 204, one or more of the one or more QC databases 106, the one ormore database server devices 114 (including, but not limited to, one ormore data storage devices communicatively coupled, either directly orindirectly, to the one or more database server devices 114), the one ormore remote server devices 116, the one or more server devices110A-110N, the one or more client devices 112A-112N, any other suitabledevice or circuitry, or a combination thereof.

In some embodiments, the QC optimization circuitry 228 includes hardwarecomponents designed or configured to request, receive, process,generate, and transmit data, data structures, control signals, andelectronic information for use in QC based optimization. In someembodiments, the QC optimization circuitry 228 may be configured tooptimize, based on optimization factor data, filtered portfoliooptimization factor data, filtered efficient frontier factor data,filtered tracking error factor data, filtered stress testing factordata, filtered hurricane factor data, and/or one or more machinelearning techniques to, optimize a portfolio. In some embodiments, theQC optimization circuitry 228 may retrieve optimization factor data,filtered portfolio optimization factor data, filtered efficient frontierfactor data, filtered tracking error factor data, filtered stresstesting factor data, filtered hurricane factor data, or the like, from adata storage device, such as memory 204, one or more of the one or moreQC databases 106, the one or more database server devices 114(including, but not limited to, one or more data storage devicescommunicatively coupled, either directly or indirectly, to the one ormore database server devices 114), the one or more remote server devices116, the one or more server devices 110A-110N, the one or more clientdevices 112A-112N, any other suitable device or circuitry, or acombination thereof.

In some embodiments, the code identification circuitry 230 includeshardware components designed or configured to request, receive, process,generate, and transmit data, data structures, control signals, andelectronic information for use in any application. In some embodiments,the code identification circuitry 230 may be configured to identifyportions (e.g., runtime hotspots and targeted portions) of code thatneed to be optimized using QC. In some embodiments, the codeidentification circuitry 230 may retrieve data from a data storagedevice, such as memory 204, one or more of the one or more QC databases106, the one or more database server devices 114 (including, but notlimited to, one or more data storage devices communicatively coupled,either directly or indirectly, to the one or more database serverdevices 114), the one or more remote server devices 116, the one or moreserver devices 110A-110N, the one or more client devices 112A-112N, anyother suitable device or circuitry, or a combination thereof. In someembodiments, the code identification circuitry 230 may be configured toutilize application performance monitoring (APM) to evaluate code tofind runtime hotspots (portions of code) and identify those runtimehotspots for QC (i.e., areas that would benefit the most from QC). Insome embodiments, the code identification circuitry 230 may be runningwhile the QC system is running to identify, in real-time, runtimehotspots for QC. Additionally, the code identification circuitry 230 mayutilize a QC algorithm to identify the runtime hotspots. The QCalgorithm used to identify the runtime hotspots may be algorithms basedon one or more of Quadratic Unconstrained Binary Optimization “QUBO”,Quantum Approximate Optimization Algorithm “QAOA”, Quantum MachineLearning “QML”, Quantum Geometrodynamics “QGD”, Quantum Monte Carlo“QMC”, Harrow, Hassidim and Lloyd “HHL”, or the like. In someembodiments, the code being optimized may be related to QC basedportfolio optimization. In some embodiments, the code identificationcircuitry 230 may receive performance information of optimizedidentified runtime hotspot from the code testing circuitry 218 and mayfurther identify runtime hotspots accordingly.

The algorithm performance circuitry 232 includes hardware componentsdesigned or configured to request, receive, process, generate, andtransmit data, data structures, control signals, and electronicinformation for use in QC applications. In some embodiments, thealgorithm performance circuitry 232 may be configured to retrieveperformance information associated with a set of non-QC algorithms, aset of QC algorithms, or both. In some embodiments, the algorithmperformance circuitry 232 may be configured to evaluate the performanceof non-QC algorithms, QC algorithms, or both when non-QC algorithms, QCalgorithms, or both are utilized for optimization, which may include thegeneration of performance information related to the non-QC algorithms,QC algorithms, or both. The algorithm performance circuitry 232 maystore a catalog of QC algorithms and associated performance information.

The algorithm selection circuitry 234 includes hardware componentsdesigned or configured to request, receive, process, generate, andtransmit data, data structures, control signals, and electronicinformation for use in QC applications. In some embodiments, thealgorithm selection circuitry 234 may receive a catalog of QC algorithmsand associated performance information from the algorithm performancecircuitry 232 for selecting QC algorithms. In some embodiments, thealgorithm selection circuitry 234 may select one QC algorithm for eachoptimization, factor to be optimized, or data to be optimized. Theselection may be based on, among other things, the determination to begenerated from the optimization, the factor to be optimized, or the datato be optimized. In some embodiments, each QC algorithm may be definedto be associated with a distinct defined hardware.

The code performance evaluation circuitry 254 includes hardwarecomponents designed or configured to request, receive, process,generate, and transmit data, data structures, control signals, andelectronic information for evaluating performance of QC or non-QC basedcode. In some embodiments, the code evaluation circuitry 254 may beconfigured to reverse engineer the code and perform static evaluation ofthe code. In some embodiments, the code performance evaluation circuitry254 may compare the performance of the code with performance of one ormore QC algorithms. In some embodiments, the code performance evaluationcircuitry 232 may be configured to evaluate the performance of QC ornon-QC based code, which may include the generation of code performanceinformation related to the QC or non-QC based code, which may includeinformation related to how code performed on a QC or non-QC basedmachine (e.g., errors generated, hot-spots, time to run, utilization ofQC or non-QC system resources), the QC run costs, the algorithm used bythe QC or non-QC based code. In some embodiments, the code performanceevaluation circuitry 254 may identify a hotspot type associated with thecode.

The machine learning circuitry 252 includes hardware components designedor configured to request, receive, process, generate, and transmit data,data structures, control signals, and electronic information forutilizing one or more machine learning models to evaluate code andalgorithms, such as by evaluating performance information, includingalgorithm performance information and code performance information. Themachine learning circuitry 252 may further receive optimization factordata and human input that may be used to evaluate code and algorithms.

The data storage circuitry 256 includes hardware components designed orconfigured to request, receive, process, generate, store, and transmitdata, data structures, control signals, and electronic information foruse in PQC. In some embodiments, the data storage circuitry 256 may beconfigured to store data (e.g., unencrypted data, encrypted data,decrypted data, re-encrypted data, double encrypted data, data accesscontrol information, bitstreams of data, links or pointers thereto),data attributes, data envelopes, enveloped data structures, policyinformation, non-QC algorithms, non-QC algorithm performanceinformation, non-PQC encryption attributes, QC algorithm performanceinformation, QC algorithms, any other suitable data or electronicinformation, or combinations thereof in a data storage device, adatabase management system, any other suitable storage device or system,or any combination thereof.

For example, the data storage circuitry 256 may be configured to storean enveloped data structure in a data storage device, a databasemanagement system, or a combination thereof. In some embodiments, thedata storage circuitry 256 may be configured to store the data, datastructures, control signals, and electronic information in the datastorage device, the database management system, or both in real-time andwithout user interactivity.

In some embodiments, the data storage device may comprise, or beimplemented as, memory 204, one or more of the one or more QC databases106, the one or more database server devices 114 (including, but notlimited to, one or more data storage devices communicatively coupled,either directly or indirectly, to the one or more database serverdevices 114), the one or more remote server devices 116, the one or moreserver devices 110A-110N, the one or more client devices 112A-112N, anyother suitable device or circuitry, or a combination thereof. In someembodiments, the database management system may comprise, or beimplemented as, a database management system (DBMS), such as arelational DMBS (RDBMS) data warehouse, a first non-relational DBMS(e.g., Hadoop distributed file system (HDFS), Hbase), a secondnon-relational DBMS (e.g., content management systems), a datavisualization device, a data mart (e.g., online analytical processing(OLAP) cube), a real-time analytical RDBMS, any other suitable device orcircuitry, or a combination thereof. In some embodiments, the datastorage device, the database management system, or both may comprise, orbe implemented as, one or more decentralized storage devices, such as acloud storage device or system.

The UI circuitry 258 includes hardware components designed or configuredto generate graphical user interface (GUI) data configured to bedisplayed by a display device. For instance, the UI circuitry 258 mayinclude hardware components designed or configured to generate GUI databased on any embodiment or combination of embodiments. In someembodiments, the UI circuitry 258 may be configured to generate GUI dataand transmit the generated GUI data to the input-output circuitry 206,and the input-output circuitry 206 may be configured to receive the GUIdata and display the received GUI data on one or more display screens.

It should also be appreciated that, in some embodiments, each of thedata attribute generation circuitry 214, data envelope generationcircuitry 216, code testing circuitry 218, data monitoring circuitry220, data access monitoring circuitry 222, data zone monitoringcircuitry 224, optimization factor filtering circuitry 226, QCoptimization circuitry 228, code identification circuitry 230, algorithmperformance circuitry 232, algorithm selection circuitry 234, machinelearning circuitry 252, code performance evaluation circuitry 254, datastorage circuitry 256, and UI circuitry 258, may include one or moreseparate processors, specially configured field programmable gate array(FPGA), ASIC, or cloud utilities to perform the above functions.

In some embodiments, the hardware components described above withreference to data attribute generation circuitry 214, data envelopegeneration circuitry 216, code testing circuitry 218, data monitoringcircuitry 220, data access monitoring circuitry 222, data zonemonitoring circuitry 224, optimization factor filtering circuitry 226,QC optimization circuitry 228, code identification circuitry 230,algorithm performance circuitry 232, algorithm selection circuitry 234,machine learning circuitry 252, code performance evaluation circuitry254, data storage circuitry 256, and UI circuitry 258, may, forinstance, communications circuitry 208, or any suitable wired orwireless communications path to communicate with a node device, a serverdevice (e.g., one or more of server devices 110A-110N), a client device(e.g., one or more of client devices 112A-112N), a database serverdevice (e.g., one or more of database server devices 114), a remoteserver device (e.g., one or more of remote server devices 116),processing circuitry 202, memory 204, input-output circuitry 206, or anyother suitable circuitry or device.

In some embodiments, one or more of the data attribute generationcircuitry 214, data envelope generation circuitry 216, code testingcircuitry 218, data monitoring circuitry 220, data access monitoringcircuitry 222, data zone monitoring circuitry 224, optimization factorfiltering circuitry 226, QC optimization circuitry 228, codeidentification circuitry 230, algorithm performance circuitry 232,algorithm selection circuitry 234, machine learning circuitry 252, codeperformance evaluation circuitry 254, data storage circuitry 256, and UIcircuitry 258 may be hosted locally by the apparatus 200.

In some embodiments, one or more of the data attribute generationcircuitry 214, data envelope generation circuitry 216, code testingcircuitry 218, data monitoring circuitry 220, data access monitoringcircuitry 222, data zone monitoring circuitry 224, optimization factorfiltering circuitry 226, QC optimization circuitry 228, codeidentification circuitry 230, algorithm performance circuitry 232,algorithm selection circuitry 234, machine learning circuitry 252, codeperformance evaluation circuitry 254, data storage circuitry 256, and UIcircuitry 258 may be hosted remotely (e.g., by one or more cloudservers) and thus need not physically reside on the apparatus 200. Thus,some or all of the functionality described herein may be provided by athird-party circuitry. For example, the apparatus 200 may access one ormore third-party circuitries via a networked connection configured totransmit and receive data and electronic information between theapparatus 200 and the third-party circuitries. In turn, the apparatus200 may be in remote communication with one or more of data attributegeneration circuitry 214, data envelope generation circuitry 216, codetesting circuitry 218, data monitoring circuitry 220, data accessmonitoring circuitry 222, data zone monitoring circuitry 224,optimization factor filtering circuitry 226, QC optimization circuitry228, code identification circuitry 230, algorithm performance circuitry232, algorithm selection circuitry 234, machine learning circuitry 252,code performance evaluation circuitry 254, data storage circuitry 256,and UI circuitry 258.

Although some of these components of apparatus 200 are described withrespect to their functional capabilities, it should be understood thatthe particular implementations necessarily include the use of particularhardware to implement such functional capabilities. It should also beunderstood that certain of these components may include similar orcommon hardware. For example, two sets of circuitries may both leverageuse of the same processor, network interface, quantum communicationsinterface, optoelectronic components, storage medium, machine learningcircuitry, or the like to perform their associated functions, such thatduplicate hardware is not required for each set of circuitries. Itshould also be appreciated that, in some embodiments, one or more ofthese components may include a separate processor, specially configuredFPGA, ASIC, or cloud utility to perform its corresponding functions asdescribed herein.

The use of the term “circuitry” as used herein with respect tocomponents of apparatus 200 includes particular hardware configured toperform the functions associated with respective circuitry describedherein. While the term “circuitry” should be understood broadly toinclude hardware, in some embodiments, circuitry may also includesoftware for configuring the hardware. For example, in some embodiments,“circuitry” may include processing circuitry, storage media, networkinterfaces, quantum interfaces, input-output devices, optoelectroniccomponents, and other components. In some embodiments, other elements ofapparatus 200 may provide or supplement the functionality of particularcircuitry. For example, the processing circuitry 202 may provideprocessing functionality, memory 204 may provide storage functionality,classical communications circuitry 210 may provide network interfacefunctionality, and quantum communications circuitry 212 may providequantum interface functionality among other features.

In some embodiments, various components of one or more of the apparatus200 may be hosted remotely (e.g., by one or more cloud servers) and thusneed not physically reside on the corresponding apparatus 200. Thus,some or all of the functionality described herein may be provided bythird-party circuitry. For example, an apparatus 200 may access one ormore third-party circuitries via any sort of networked connection thatfacilitates transmission of data and electronic information between theapparatus 200 and the third-party circuitries. In turn, that apparatus200 may be in remote communication with one or more of the othercomponents described above as being comprised by the apparatus 200.

As will be appreciated, computer program instructions and/or other typeof code may be loaded onto a computer, processor or other programmableapparatus's circuitry to produce a machine, such that the computer,processor, or other programmable circuitry that executes the code on themachine creates the means for implementing various functions describedherein.

As described above and as will be appreciated based on this disclosure,embodiments of the present disclosure may be configured as systems,apparatuses, methods, optoelectronic devices, mobile devices, backendnetwork devices, computer program products, other suitable devices, andcombinations thereof. Accordingly, embodiments may comprise variousmeans including entirely of hardware or any combination of software withhardware. Furthermore, embodiments may take the form of a computerprogram product on at least one non-transitory computer-readable storagemedium having computer-readable program instructions (e.g., computersoftware) embodied in the storage medium. Any suitable computer-readablestorage medium may be utilized including non-transitory hard disks,CD-ROMs, flash memory, optical storage devices, or magnetic storagedevices. As will be appreciated, any computer-executable program codeinstructions, any other type of code described herein, and anycombination thereof may be loaded onto a computer, processor or otherprogrammable apparatus's circuitry to produce a machine, such that thecomputer, processor, or other programmable circuitry that executes thecode on the machine creates the means for implementing variousfunctions, including the functions described herein.

The one or more server devices 110A-110N, one or more client devices112A-112N, one or more database server devices 114, and one or moreremote server devices 116 described with reference to FIG. 1 may beembodied by one or more computing devices, servers, data storagedevices, or systems that also may include processing circuitry, memory,input-output circuitry, and communications circuitry. For example, aserver device 110 may be a database server on which computer code (e.g.,C, C++, C#, java, a structured query language (SQL), a data querylanguage (DQL), a data definition language (DDL), a data controllanguage (DCL), a data manipulation language (DML)) is running orotherwise being executed by processing circuitry. In another example, aclient device 112 may be a smartphone on which an app (e.g., a mobiledatabase app) is running or otherwise being executed by processingcircuitry. As it relates to operations described in the presentdisclosure, the functioning of these devices may utilize componentssimilar to the similarly named components described above with referenceto FIG. 2 . Additional description of the mechanics of these componentsis omitted for the sake of brevity. These device elements, operatingtogether, provide the respective computing systems with thefunctionality necessary to facilitate the communication of data with thePQC system described herein.

Having described specific components of example devices and circuitriesinvolved in various embodiments contemplated herein, example proceduresfor QC based optimization are described below in connection with FIGS.3-7 .

FIGS. 3 and 5-7 illustrates an example flowcharts 300 that containoperations for generating a determination in accordance with someexample embodiments described herein. The operations illustrated inFIGS. 3 and 5-7 may, for example, be performed by one or more componentsdescribed with reference to QC system 102 shown in FIG. 1 ; by a serverdevice 110, a client device 112, a database server device 114, or aremote server device 116 in communication with QC system 102; byapparatus 200 shown in FIG. 2 ; or by any combination thereof. In someembodiments, the various operations described in connection with FIG. 3may be performed by the apparatus 200 by or through the use of one ormore of processing circuitry 202, memory 204, input-output circuitry206, communications circuitry 208 (including, but not limited to,classical communications circuitry 210 and quantum communicationscircuitry 212), data attribute generation circuitry 214, data envelopegeneration circuitry 216, code testing circuitry 218, data monitoringcircuitry 220 (including, but not limited to, data access monitoringcircuitry 222 and data zone monitoring circuitry 224), optimizationfactor filtering circuitry 226, QC optimization circuitry 228, codeidentification circuitry 230, algorithm performance circuitry 232,algorithm selection circuitry 234, machine learning circuitry 252, codeperformance evaluation circuitry 254, data storage circuitry 256, userinterface (UI) circuitry 258, any other suitable circuitry, and anycombination thereof.

FIGS. 3 and 5-7 illustrates flowcharts describing the operation ofvarious systems (e.g., QC system 102 described with reference to FIG. 1), apparatuses (e.g., apparatus 200 described with reference to FIG. 2), methods (e.g., flowchart 300 described with reference to FIG. 3 ),and computer program products according to example embodimentscontemplated herein. It will be understood that each operation of theflowcharts, and combinations of operations in the flowcharts, may beimplemented by various means, such as hardware, firmware, processor,circuitry, and/or other devices associated with execution of softwareincluding one or more computer program instructions. For example, one ormore of the procedures described herein may be performed by execution ofcomputer program instructions. In this regard, the computer programinstructions that, when executed, cause performance of the proceduresdescribed above may be stored by a memory (e.g., memory 204) of anapparatus (e.g., apparatus 200) and executed by a processor (e.g.,processing circuitry 202) of the apparatus. As will be appreciated, anysuch computer program instructions may be loaded onto a computer orother programmable apparatus (e.g., hardware) to produce a machine, suchthat the resulting computer or other programmable apparatus implementsthe functions specified in the flowchart operations. These computerprogram instructions may also be stored in a computer-readable memorythat may direct a computer or other programmable apparatus to functionin a particular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture, theexecution of which implements the functions specified in the flowchartoperations. The computer program instructions may also be loaded onto acomputer or other programmable apparatus to cause a series of operationsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructionsexecuted on the computer or other programmable apparatus provideoperations for implementing the functions specified in the flowchartoperations.

The flowchart operations described with reference to FIGS. 3 and 5-7support combinations of means for performing the specified functions andcombinations of operations for performing the specified functions. Itwill be understood that one or more operations of the flowcharts, andcombinations of operations in the flowcharts, may be implemented byspecial purpose hardware-based computer systems which perform thespecified functions, or combinations of special purpose hardware andcomputer instructions.

Portfolio Optimization

FIG. 3 is a flowchart showing exemplary operations for optimizing anoptimization determination based on QC algorithms in accordance withsome example embodiments described herein. A portfolio of assets may beoptimized for a plurality of factors, such as for a given risk level,which may be for a given period of time. A period of time may be hours,days, weeks, months, or years. A period of time may, or may not, relateto ages, life events, career events (e.g., retirement), and/or exogenousevents. A risk level may be based on a scale of how risky an asset isconsidered, such as a ranking of one to ten, where a rank of one mayrepresent a low risk asset such as cash and ten may rank as a high-riskasset such as options. Additionally, or alternatively, risk may be basedon desired value of the portfolio at the end of a period of time in viewof a historic and/or forecasted rate of growth. Additionally, oralternatively, a portfolio may be compared to a benchmark, which may befor returns and/or risk, which may allow for a determination if thedesired risk is more or less risky than the benchmark.

Additionally, or alternatively, a portfolio may be optimized based onfactors related to optimization factor data, such as a portfolio owner'sconstraints, preferences and/or life events. Additionally, oralternatively, a portfolio may be optimized based on events external tothe customer, such as local events, national events, and/or worldevents. Additionally, or alternatively, a portfolio may be personalizedto create a personalized portfolio by the portfolio owner choosingconstraints and/or preferences for the portfolio, which may include theportfolio providing feedback about or specifying choices or values forsome of the optimization factors. Additionally, or alternatively, thefeedback may be related to questions posed to the portfolio owner, thefeedback on which may specify scenarios, risks, returns, or timeperiods. The optimization of the portfolio may provide for the portfolioto achieve the portfolio owner's constraints and/or preference, whichmay be to maximize growth, minimize volatility, attain a value, minimizerisk, and/or a combination these goals. Constraints and/or preferencemay change over time, such as with a portfolio owner reach certain lifeevents, such as graduating school, obtaining a job, getting married,having children, caring for others, and/or retirement. Constraintsand/or preferences may also change based on a nonlife events and mayinclude owning assets or not owning assets associated with a specifiedlocation, owning assets or not owning assets at certain times, owningassets or not owning assets associated with a specified industry orindustries, owning assets or not owning assets related to specifiedproducts, and/or owning assets or not owning assets related to entities,organizations, and/or businesses.

In some embodiments, a portfolio may include plurality of types ofassets. The type of assets may change over time, such as in response tochanges in risk level, constraints, preferences, life events, orexternal events. Such changes may require rebalancing a portfolio, whichmay be done to optimize the portfolio.

Optimizing the portfolio may require analysis of a plurality ofoptimization factors that depend on the assets of the portfolio.Depending on the optimization factors associated with the portfolio'sassets, optimizing the portfolio may include forecasting risk andreturns associated with each asset, which may include how the risk ofassets in the portfolio relate to each other.

Optimization may be performed with QC algorithms. In some exampleembodiments, the QC algorithms to optimize may include algorithms todetermine the performance of an asset, determine the performance of theportfolio, and/or determine how assets in the portfolio relate to eachother to achieve an optimization. These QC algorithms for each asset ofthe portfolio may be the same or they may be different. Additionally, oralternatively, the QC algorithm to be used may be selected based on,among other things, optimization factors, including a selected timeperiod, risk level, portfolio owner's constraints, portfolio owner'spreferences, portfolio owner's life events, and/or external events.

In an embodiment, a first portfolio may include only stocks, which maylead to a selection of a first QC algorithm that provides betterperformance in optimizing stocks based on the optimization factorsrelated to stocks.

In another embodiment, a second portfolio may include commodities, whichmay lead to a selection of a second QC algorithm that provides betterperformance in optimizing commodities based on the optimization factorsrelated to commodities.

In another embodiment, a third portfolio may include stocks andcommodities and may lead to the selection of a first QC algorithm thatprovides better performance in optimizing stocks and a second QCalgorithm that provides better performance in optimizing commodities,which may include each QC algorithm providing better performance basedon the optimization factors associated with, respectively, stocks andcommodities.

In another embodiment, a fourth portfolio may include only stocks and atime period may be selected that allows for some factors that wouldotherwise be relevant to an optimization to be omitted, which may leadto the selection of a fourth QC algorithm that provides betterperformance in optimizing stocks based on the optimization factorsassociated with optimizing the portfolio for the time period selected.

In another embodiment, a fifth portfolio may include only stocks and arisk level may be selected that allows for some factors that wouldotherwise be relevant an optimization to be omitted, which may lead tothe selection of a fifth QC algorithm that provides better performancein optimizing stocks based on the optimization factors associated withoptimizing the portfolio for the risk level selected.

In an embodiment, performance information for an algorithm may be usedand/or tracked for the selection of a QC algorithm or, alternatively, ofthe selection of an alternative QC algorithm to perform an optimization,which may be based on how either of the QC algorithms previouslyperformed, how either of the QC algorithms performed in testing,performance when varying QC system resources are available, performanceof QC run costs, and/or performance for variations of code running theQC algorithm.

Returning to the operations of FIG. 3 , as shown by operation 302, theapparatus 200 includes means, such as code identification circuitry 230or the like, for identifying filtered portfolio optimization factor databased on one or more of optimization factor data, QC algorithms,algorithm performance information, and code performance information.

The one or more QC algorithms may be one or more of, for example,Quadratic Unconstrained Binary Optimization “QUBO”, Quantum ApproximateOptimization Algorithm “QAOA”, Quantum Machine Learning “QML”, QuantumGeometrodynamics “QGD”, Quantum Monte Carlo “QMC”, Harrow, Hassidim andLloyd “HHL”, or the like, executed on a particular QC machine (e.g.,quantum annealer, circuit-based quantum processor, or the like).

As shown by operation 304, the apparatus 200 includes means, such asalgorithm selection circuitry 234 or the like, for selecting one or moreQC algorithms for the filtered portfolio optimization factor data. Insome embodiments, the algorithm selection circuitry 234 may receiveresults of comparing the filtered portfolio optimization factor datawith performance of one or more QC algorithms and/or performance ofcode, and a QC algorithm will be selected accordingly.

As shown by operation 306, the apparatus 200 includes means, such as QCoptimization circuitry 228, for utilizing the selected QC algorithm(s)to optimize the portfolio for the filtered portfolio optimization factordata to generate an optimization determination. An optimizationdetermination will provide what was determined may be done to optimizethe portfolio, which may include any assumptions, forecasts, models, orfactor data utilized by the QC algorithm in generating thedetermination.

As shown by operation 308, the apparatus 200 includes means, such as QCoptimization circuitry 228 in conjunction with communications circuitry208, for rebalancing the portfolio according to the optimizationdetermination. The apparatus 200 may cause order to be generated andsent that may buy and/or sell assets in order to rebalance theportfolio. The buying and/or selling of assets may change the assets,the quantity of assets, and/or the type of assets in the portfolio tooptimize the portfolio in accordance with the portfolio optimizationdetermination.

In another embodiment, the apparatus 200, such as with QC optimizationcircuitry 228, may generate more than one optimization determinationbased on the selected QC algorithms and the filtered portfoliooptimization data, with each of the optimization determinationssatisfying a desired optimization factor, such as given risk level,while also satisfying another optimization factor, such as a level ofreturn. The multiple optimization determinations may, for example,differ in their impact to the portfolio in regard to anotheroptimization factor, such as differing in assets that may have differenttax consequences.

The portfolio optimization determination may indicate that the portfolioalready has the optimal allocation of assets for a plurality of factors,such as for a given risk level, which may be for a given period of time.Alternatively, the portfolio optimization determination may indicatethat the portfolio is not optimized, and a portfolio owner may bealerted to this portfolio optimization determination and/or theportfolio may be rebalanced. Additionally, or alternatively, if theportfolio is determined to not be optimized, the portfolio optimizationdetermination may include what is required to optimize the portfoliobased on a time period selected, a risk level, the portfolio owner'sconstraints, the portfolio owner's preference, life events and/orexternal events.

In some embodiments, the portfolio owner will be alerted of theportfolio optimization determination and provide input results in theportfolio being rebalanced. In other embodiments, a portfoliooptimization determination may trigger the portfolio being rebalanced.In some embodiments, a portfolio owner may set a preference if an alertshould be provided or if the portfolio should be rebalanced prior to theportfolio owner receiving an alert or if not alert should be sent.

Portfolio Optimization for an Efficient Frontier

FIG. 4 illustrates an example of an efficient frontier in accordancewith some example embodiments described herein and FIG. 5 is a flowchartshowing exemplary operations for utilizing an efficient frontier torebalance a portfolio of assets in accordance with some exampleembodiments described herein. A portfolio of assets may be optimized toan efficient frontier. An efficient frontier is measured as a return fora particular level of risk, where the risk may be represented as astandard deviation. A return of the portfolio is dependent on the assetsthat are in the portfolio, including the quantity of each asset in theportfolio. Portfolios that are not on the efficient frontier may bereferred to as sub-optimal because they either do not provide enoughreturn for a level of risk, or have too much risk for a level of return,or both. As a portfolio owner select a given level of return or a givenlevel of risk, the portfolio owner may not be on the efficient frontierfor the portfolio and the portfolio may need to be rebalanced.

As stated above, FIG. 4 illustrates an example of an efficient frontierin accordance with some example embodiments described herein. In FIG. 4, an efficient frontier 402 is an optimal amount of return for a levelof risk. For example, portfolio 410 is on the efficient frontier and hasan optimal amount of return for the amount of risk associated with theassets in portfolio 410, which may be referred to as an optimal riskyportfolio. The point where the efficient frontier 402 intersects with acapital market line 412 may be where portfolio 410 has an optimal amountof return for the amount of risk. The capital market line 412 may bedetermined based on a risk free rate 414, and the risk free rate may notbe constant. For example, the risk free rate may be specific to aparticular efficient frontier, including, but not limited to, beingspecific to an asset, a portfolio, or a company. In contrast, and asillustrated in FIG. 4 , asset 420, asset 422, and asset 424 may each besub-optimal in that each, for the level of risk, may achieve asub-optimal return. An efficient frontier may be obtained by optimizingan efficient frontier determination regarding an optimal portfolio,including the quantity of each asset in the portfolio (e.g., number ofshares of stock, number of bonds, etc.) for a specific amount of risk.In some embodiments, reference numbers 420, 422, and 424 may not beindividual assets but instead may be include, but are not limited to,portfolios or other groupings of assets.

In FIG. 5 , as shown by operation 502, the apparatus 200 includes means,such as code identification circuitry 230 or the like, for identifyingfiltered portfolio optimization factor data based on one or more ofoptimization factor data, QC algorithms, algorithm performanceinformation, and code performance information.

The one or more QC algorithms may be one or more of, for example,Quadratic Unconstrained Binary Optimization “QUBO”, Quantum ApproximateOptimization Algorithm “QAOA”, Quantum Machine Learning “QML”, QuantumGeometrodynamics “QGD”, Quantum Monte Carlo “QMC”, Harrow, Hassidim andLloyd “HHL”, or the like, executed on a particular QC machine (e.g.,quantum annealer, circuit-based quantum processor, or the like). The QCalgorithm may depend on the hardware and/or software of quantumcomputing system 102. In one embodiment, the hardware and/or softwaremay include an annealer or a circuit bases universal gate model, and theQC algorithm may depend on such hardware and/or software.

As shown by operation 504, the apparatus 200 includes means, such asalgorithm selection circuitry 234 or the like, for selecting one or moreQC algorithms for the filtered portfolio optimization factor data. Insome embodiments, the algorithm selection circuitry 234 may receiveresults of comparing the filtered portfolio optimization factor datawith performance of one or more QC algorithms and/or performance ofcode, and a QC algorithm will be selected accordingly.

As shown by operation 506, the apparatus 200 includes means, such as QCoptimization circuitry 228, for utilizing the selected QC algorithm(s)to optimize the portfolio for the filtered portfolio optimization factordata to generate an efficient frontier determination. An efficientfrontier determination will provide what was determined to be theportfolio for achieving optimal returns at a particular risk level basedon the assets at a particular risk level, which may include anyassumptions, forecasts, models, or factor data utilized by the QCalgorithm in generating the determination.

As shown by operation 508, the apparatus 200 includes means, such as QCoptimization circuitry 228 in conjunction with communications circuitry208, for rebalancing the portfolio according to the efficient frontierdetermination. The apparatus 200 may cause order to be generated andsent that may buy and/or sell assets in order to rebalance theportfolio. The buying and/or selling of assets may change the assets,the quantity of assets, and/or the type of assets in the portfolio tooptimize the portfolio in accordance with the efficient frontierdetermination.

Tracking Error

FIG. 6 is a flowchart showing exemplary operations for optimizing atracking error determination based on QC algorithms in accordance withsome example embodiments described herein A portfolio may attempt totrack a benchmark, and the difference between the performance of theportfolio to the performance of the benchmark may be determined by thetracking error. A portfolio may be a single asset or it may be a bundleof assets. The tracking error is the divergence between, for example,the price or returns of a portfolio and the price or returns of abenchmark. Tracking error may be reported as a standard deviationpercentage difference between a portfolio and a benchmark, which reportsthe difference between the return received by the portfolio and that ofthe benchmark.

A benchmark may a single asset, a bundle of assets, or an index. In oneembodiment, the benchmark may a stock market index, such as the DowJones Industrial Average, the NASDAQ Composite Index, the S&P 500 Index,or the Russell 2000 Index. In another embodiment, the benchmark may be amultiple of an index or another portfolio, such as two times the S&P 500Index or minus one times the S&P Index. In another embodiment, thebenchmark may be the value of an asset, such as the value of a house orvalue or a piece of art. In another embodiment, the benchmark may be thevalue of another's portfolio or of another's assets. In anotherembodiment, the benchmark may be another's investments, such as theinvestments of a company or professional investor. In one embodiment, aportfolio may be a mutual fund that seeks to benchmark the performanceof the S&P 500 Index, and the portfolio may include the same stocks asincluded in the S&P 500 Index or it may include different stocks fromthe S&P 500 Index. Additionally, or alternatively, a portfolio mayinclude different amounts or weightings of assets than the benchmark itis tracking.

A tracking error may be determined over a historical period of time. Atracking error for a historical period of time may define the timeperiod and then, based on the performance of the portfolio and theperformance of the benchmark over that time period, determine thetracking error.

A tracking error may be determined for a forecast of a portfolio and aforecast of a benchmark. The forecast for the portfolio may beforecasted separately or together with the forecast for the benchmark. Aforecast may be determined by selecting a period of time and thenevaluating how, for example, a portfolio or a benchmark may change overthe selected period of time. How a portfolio or a benchmark may changedepends on what type of assets makes up the portfolio or benchmark. Inone embodiment, the asset or assets that make up the portfolio or thebenchmark may be the same type of assets, may share some types ofassets, or may not share any types of assets.

In an embodiment with the portfolio and benchmark being of the sametypes of assets, the factors used to determine a forecast of theportfolio and the forecast of the benchmark may use the same. In anotherembodiment with the portfolio and benchmark may share some but not alltypes of assets, the factors used to determine each forecast may be thesame or similar for determining a forecast of one asset and may bedifferent for determining a forecast of another asset. In an embodimentwith the portfolio and benchmark may not share any types of assets, thefactors used to determine each forecast be different.

A portfolio may include a plurality of different assets, and thequantity of factors used to determine a forecast for the portfolio mayvary. In one embodiment, a portfolio may contain stocks. In anotherembodiment, a portfolio may contain stocks and bonds. In anotherembodiment, a portfolio may contain stock, bonds, cash, and options. Inanother embodiment, a portfolio may contain stock, bonds, cash, andcommodities. In one embodiment, a forecast of a stock may includefactors related to earnings, interest rates, default risk, dividends,stock buybacks, tax rates, and/or government approvals, among others. Inanother embodiment, a forecast of a commodity may include factorsrelated to interest rates, tax rates, weather patterns, supply of thecommodity, demand of the commodity, cost to produce the commodity,geopolitical risk associated with the commodity, public sentiment,and/or governmental approvals, among others.

Additionally, for a selected time period for the forecast, some of thefactor may change over the time period while others may stay the same.In an embodiment, if a factor stays the same, the forecast may omit thefactor.

The determination of the forecast tracking error may be determined by QCalgorithms. In an embodiment, the QC algorithms to determine theforecast tracking error may include QC algorithms to determine theforecast of the portfolio, determine the forecast of the benchmark,and/or determine the forecast tracking error. These QC algorithms may bethe same or they may be different, which may depend on the assets in theportfolio and the benchmark. The QC algorithm to be used may be selectedbased on, among other things, the portfolio or the benchmark. Forexample, a QC algorithm to forecast the returns of a stock, which mayaddress factors related to the stock (e.g., earnings, interest rates,default risk, dividends, stock buybacks, tax rates, and/or governmentapprovals, etc.) may be different from a QC algorithm to forecast thereturns for a commodity, which may address factors related to thecommodity (e.g., interest rates, tax rates, weather patterns, supply ofthe commodity, demand of the commodity, cost to produce the commodity,geopolitical risk associated with the commodity, public sentiment,and/or governmental approvals). The resources required to run these QCalgorithms, such as the QC run cost, may also differ, includingdifferent due to the number and type of factors evaluated by the QCalgorithm.

In an embodiment, performance information for an algorithm may be usedand/or tracked for the selection of a QC algorithm or, alternatively, ofthe selection of an alternative QC algorithm to perform an optimization,which may be based on how either of the QC algorithms previouslyperformed, how either of the QC algorithms performed in testing, QCsystem resources available, QC run costs, and/or code running the QCalgorithm.

In FIG. 6 , as shown by operation 602, the apparatus 200 includes means,such as code identification circuitry 230 or the like, for identifyingtracking error factor data for a portfolio and a benchmark based on oneor more of optimization factor data, QC algorithms, algorithmperformance information, and code performance information.

The one or more QC algorithms may be one or more of, for example,Quadratic Unconstrained Binary Optimization “QUBO”, Quantum ApproximateOptimization Algorithm “QAOA”, Quantum Machine Learning “QML”, QuantumGeometrodynamics “QGD”, Quantum Monte Carlo “QMC”, Harrow, Hassidim andLloyd “HHL”, or the like, executed on a particular QC machine (e.g.,quantum annealer, circuit-based quantum processor, or the like).

As shown by operation 604, the apparatus 200 includes means, such asalgorithm selection circuitry 234 or the like, for selecting one or moreQC algorithms for the forecast tracking error factor data for generatinga tracking error determination. In some embodiments, the algorithmselection circuitry 234 may receive results of comparing the forecasttracking error factor data with performance of one or more QC algorithmsand/or performance of code, and a QC algorithm will be selectedaccordingly.

As shown by operation 606, the apparatus 200 includes means, such as QCoptimization circuitry 228, for utilizing the selected QC algorithm(s)to generate a tracking error determination form the forecast trackingerror factor data. A tracking error determination will provide what wasdetermined may be the tracking error along with what may have beendetermined to have created the error, which may include any assumptions,forecasts, models, or factor data utilized by the QC algorithm ingenerating the determination. For an example of a portfolio of stocksand a benchmark of a stock index, a tracking error may have beendetermined because a portfolio or, alternatively, a benchmark may havedistributed dividends or undergone share buyback when the other did not.In another example, involving a portfolio of real estate investmenttrusts and a benchmark of real estate, either the portfolio may have haddividends while the real estate did not or the real estate may have beenimpacted by a natural disaster while the portfolio was not.

In some embodiments, a tracking error or a forecast tracking error maybe determined in real-time or, alternatively, the tracking error may bedetermined on a periodic basis (e.g., market open, market close, everyhour, or every night). Additionally, or alternatively, a tracking erroror forecast tracking error may be determined if a factor associated withthe portfolio moves in relation to a threshold or a range, which may ormay not be in relation to a previous value (e.g., a factor change inprice of 2% since market open). In some embodiments, a tracking errorgreater than a threshold or outside of a range may result in arebalancing of the portfolio, which is described herein. In someembodiments, a tracking error greater than a threshold or outside of arange may generate an alert, which is described herein.

Stress Testing

FIG. 7 is a flowchart showing exemplary operations for performing astress testing determination based on QC algorithms in accordance withsome example embodiments described herein Stress testing may beperformed on an asset, a portfolio (e.g., a portfolio of a customer, aportfolio within a company, or a portfolio of some (e.g., 25%, 50%, or75%) or all of other portfolios), or a company, and stress testing may,or may not, be performed during optimization. Stress testing may includemultiple scenarios and/or forecasts to evaluate the financial health ofan asset, a portfolio, or a company. Stress testing may assess multiplefactors, which may include optimization factors. While the stresstesting may be described as being associated with a company, the sametype of stress testing may be associated with an asset or portfolio.

Stress testing of a company may include the stress testing of assets,liabilities, and/or decisions the company may make about some or all ofthose assets and/or liabilities. One example of such stress testing isComprehensive Capital Analysis and Review (CCAR), which involves theevaluation of the internal capital planning processes of large, complexbank holding companies and their proposals to undertake capital actions,such as increasing dividend payments or repurchasing or redeeming stock.Another example of such stress testing is the Dodd-Frank Act StressTests (DFAST), which involves the evaluation of the impact on capitallevels from immediate financial shocks and for a period of time ofadverse economic conditions. A further example of stress testing isBasel III, which involves evaluation of, among of things, capitaladequacy and market liquidity.

Guidelines may be issued for mandated types of stress testing and, whenstress testing is mandated, reports may be generated from the stresstesting to demonstrate the results. CCAR guidelines are issued by theFederal Reserve to test that certain companies hold adequate capital tomaintain ready access to funding, continue operations, and meet theirobligations to creditors and counterparties, and continue to serve ascredit intermediaries, even under adverse conditions. Companies subjectto CCAR may submit comprehensive capital plans and additionalsupervisory information. A comprehensive capital plan may addressmultiple criteria, including: (1) capital assessment and planningprocesses; (2) capital distribution policy; (3) plans to repay anygovernment investment; (4) ability to absorb losses under severalscenarios; and (5) plans for addressing the expected impact ofgovernment legislation. DFAST guidelines are issued by the FederalHousing Finance Agency and Basel III guidelines are issued by the BaselCommittee on Banking Supervision of the Bank of InternationalSettlements. Companies subject to DFAST may perform stress testing undermultiple scenarios, such as baseline scenarios, adverse scenarios, andseverely adverse scenarios to assess the potential impact of economicand financial conditions on earnings, losses, and capital over a periodof time. Companies subject to Basel III may perform stress testing undermultiple scenarios, which may provide alerts of unexpected adverseoutcomes arising from risks and provide indication of the financialresources that might be needed to absorb losses from risks, such aslarge shocks. While the guidelines for each of CCAR, DFAST, and BaselIII are distinct, each is a stress test and the stress testing describedbelow is not limited to only CCAR, DFAST, or Basel III and it isunderstood that various components may be used for stress testing.Therefore, it is to be understood that the disclosure associated with aparticular stress test (e.g., CCAR, DFAST, or Basel III) is not to belimited to the specific embodiments of a stress test.

Stress testing, including but not limited to CCAR testing, may includescenarios to test an ability to absorb unexpected losses and continue tolend, which may be to test for capital adequacy. CCAR testing maydemonstrate that large bank holding companies have thorough and robustprocesses for managing and allocating their capital resources, and thatthese are supported by effective risk measurement and managementpractices. Scenarios lasting multiple years may be divided into smallertime periods, such as quarters. Stress testing may also include one ormore scenarios of a baseline scenario, an adverse scenario, a severelyadverse scenario, a loss estimation scenario, and/or a pre-provision netrevenue (PPNR) scenario.

In one embodiment, such as with CCAR testing, the stress testing mayassess capital adequacy, including an assessment of the level andcomposition of capital resources under stressed economic and financialmarket conditions. Stress testing may encompass both quantitativeanalysis and qualitative reviews of large bank holding companies'processes for assessing, and strategies for managing, their capitalresources, rather than focusing on static comparisons of current capitalamounts relative to regulatory minimum requirements, internal managementtargets, and capital levels at peer institutions.

In other embodiments, the stress testing may assess retail credit risk,wholesale credit risk, available for sale (AFS) securities,hold-to-maturity (HTM) securities, operational risk, market risk,counterparty risk, noninterest income, noninterest expense(s), netinterest income, balance sheet projections, risk weight assets (RWA),and/or allowance for loan and lease losses (ALLL). Such assessments maybe made by using models and/or approaches, including but not limited toexpected loss approach models, rating transition models, vintage lossmodels, charge-off models, ratings-based approach along with cash flowand credit analysis, historical averages, legal exposures, regressionmodels, modified latent Dirichlet allocation (LDA), probabilisticapproach, deterministic approach, volume projections with or without feeand cost rates, pricing models, prepayment rate models, interest ratemodels, re-pricing rate models, line utilization models, product mixmodels, balance or volume projection models, component models(origination, prepayment, and/or default), credit loss models, revenueand balance sheet models, production growth models, and/or depositmodels. In some embodiments, the stress testing may use scenarios and/ormodels for different portions of a company (e.g., retail versuswholesale).

Stress testing, including but not limited to CCAR, may include manyfactors, such as: (1) a description of the current regulatory capitalbase, including key contractual terms of capital instruments and anymanagement plans to retire, refinance, or replace the capitalinstruments over the planning horizon; (2) a description of all plannedcapital actions (e.g., dividends, share repurchases, and issuances), aswell as anticipated changes in the company's risk profile, businessstrategy, or corporate structure over the planning horizon; (3) adescription of the bank holding company's processes and policies fordetermining the size of dividend and common stock repurchase programsunder different operating conditions; (4) the company's assessment ofpotential losses, earnings, and other resources available to absorb suchlosses under stressed economic and financial market environments, andthe resulting impact on a company's capital adequacy and capital needsover the planning horizon; and (5) an assessment, accompanied bysupporting analysis, of the capital needed by the company on apost-stress basis to continue operations, meet its obligations, andfunction as a credit intermediary. The testing may include demonstratingan appropriate internal target level of capital and to take actionsconsistent with the maintenance of the internal target over time. Thestress testing may also involve triggers and specified actions, whichmay reduce capital distributions under adverse conditions. Stresstesting may include varying some or all of these factors and/or testingscenarios where, for example, constrains may be breached, processes arefollowed (or not), and/or other factors are varied.

Stress testing, including but not limited to CCAR, may asses additionalfactors related to a company's comprehensive capital plans: (1) CapitalAdequacy Processes (CAP) assessment—determine and evaluate if processesfor planning, managing, and allocating capital resources and forassessing whether capital is adequate to withstand a stressful economicenvironment, and if those processes are supported by adequatecompany-wide risk management and measurement practices; (2) capitaldistribution policy—determine and evaluate if policies and processesgoverning dividends, repurchases, and any other distributions in view ofconsiderations of the company's future performance; (3) governmentinvestment repayment—determine and evaluate plans will result in therepayment of any government investment before beginning or increasingcapital distributions to common shareholders; (4) stress scenarioanalysis—determine and evaluate if sufficient capital, incorporating allproposed capital actions, is had to remain viable and healthy even undera stressful economic environment; and (5) governmentlegislation—determine and evaluate if plans may meet regulatoryrequirements. The assessment may include whether dividend and repurchasepolicies enumerate quantitative and qualitative criteria for promptlyreconsidering capital distributions in the event of a deterioratingoperating environment or economic outlook. Stress testing may evaluatehow judgments on distributions were informed and were expected to differunder baseline projections and more adverse conditions. Stress testingfactors may include optimization factors related to an asset. Stresstesting may evaluate risk management capabilities, supportingsupervisory activities (e.g., inspections), and/or risk profiles.

Quarterly forecasts or projections may include revenues, losses, and proforma capital positions over a quarterly under different scenarios. Thestress test may determine quarterly projections of a company'sregulatory capital ratios, such as Tier 1 ratio, total capital ratio,and leverage ratio as well as a Tier 1 common ratio. As a part of theplans to be evaluated, it may be required to specify, by quarter, allplanned capital actions, including dividend payments, common sharerepurchases, conversions, and issuance, as well as expected changes inthe company's risk profile, business strategy, or corporate structureover the planning horizon.

Multiple scenarios may also be used to determine how a company performsfor each scenario, which may involve varying multiple factors. In someembodiments, there may be three scenarios that include (1) a baselinescenario reflecting expectations of the most likely path of the economy;(2) a stress scenario to stress key sources of revenue and the mostvulnerable sources of loss; and (3) an adverse “supervisory stressscenario” that may be generated by another, such as the Federal Reserve.The scenarios may represent a recession, with negative economic growthfor at least a couple of quarters, a rise in unemployment, and/or asharp drop in risky asset prices. The scenarios may cover the timeperiod(s) tested and may, or may not, include additional time periods.The scenarios may include factors representing, for example, a real GDP,unemployment rate, notional house price index, and/or equity priceindex. The scenarios may specify different value for each of the factorsover the duration of the scenarios, and these factors may change or maystay the same. Scenarios may also include estimates of potential lossesstemming from trading activities and private equity investments using ascenario that differs from the, for example, three scenarios. Thetesting may include estimated potential mark-to-market anddefault-related losses on trading and private equity positions and fromexposures to trading and financing transaction counterparties. Stresstesting may involve providing detailed information about loan andsecurities portfolios, the trading portfolio and its sensitivity todifferent market risk factors, and factors affecting futurerevenue-generating ability. The stress testing may also be used todevelop and calibrate loss estimation/revenue models for different typesof loans and securities, including being based on processes andempirical approaches to projecting losses, revenues, and other keycomponents affecting capital over the tested time period. Stress testingscenarios may also include varying models used to test for, for example,pro forma capital ratios and/or balance sheet models.

Stress testing, such as CCAR testing, may also include developingstrategies, projections, and/or testing scenarios regarding reducing orincreasing certain types of positions or portfolios; sales of certainportfolios, securities, or other assets; improvements in risk modeling;or other changes in business focus or operations that would affectrisk-weighted assets, leverage ratio assets, or capital. These include,but are not limited to, modeling for capital adequacy, market risk,counterparty risk, and other business risks.

Stress testing may also evaluate the processes used to manage and assessrisks and capital adequacy on an ongoing and forward-looking basis. Thisassessment may be made by varying stress testing factors to determinewhen risks or capital are no longer adequate for a scenario.

Stress testing may be performed on a real-time or a periodic basis, anda periodic basis may change time periods. Additionally, oralternatively, stress testing may be performed whenever there may be amaterial change in the company's risk profile, business strategy, orcorporate structure. Additionally, or alternatively, stress testing maybe performed upon changes in economic and financial market conditionsand relevant idiosyncratic risks, changes in the outlook for theoperating environment, and to proactively adjust capital distributionsas circumstances warrant.

Stress testing, such as CCAR testing, may include a comprehensivecapital plan which may be submitted, and such a submission may be to,for example, the Federal Reserve.

The manner in which stress testing, such as CCAR testing, is conductedmay also apply to stress testing of an asset or a portfolio. In someembodiments, the stress testing of an asset or a portfolio of assets mayinvolve factors regarding the capital decisions the business associatedwith those asset may make in response to scenarios. Such stress testing,or the factors used in the stress testing, may, or may not, be used inoptimizing a portfolio as described herein.

Stress testing may identify data gaps, which may be where data may notbe available. In some embodiments, the stress testing may includereceiving data from more than one source, such as from differentdivisions of a company or from third parties. A data gap may exist ifthe data received from each source is different, such as a one sourceomitting some of the data or some aspect of the data, including but notlimited to the data being provided in a format that may contain lessinformation. In some embodiments, data gaps may be when a scenario ormodel including data that has not been provided. In some embodiments,when stress testing identifies a data gap the stress test may be atrigger. In some embodiments, a trigger may alert that the data gapexists and proceed with the stress testing. In some embodiments, atrigger may request data not currently in the stress test be input toresolve the data gap. In some embodiments, the trigger may request datanot currently in the stress test from a server or database, which maysend requested data and, upon receipt, the stress test may incorporatethe data to resolve the data gap.

Stress testing may identify data availability, including theavailability of data at an appropriate level of granularity, such as acompany level, a portfolio level, or an asset level. In someembodiments, if data is not available at the appropriate level ofgranularity, this may be identified as a data gap. In some embodiments,if data is not available at the appropriate level of granularity, thestress test may use data available at either a higher or lower level ofgranularity. In some embodiments, if data is not available at theappropriate level of granularity, the stress test may use assumptions,which may be based on data available at either a higher or lower levelof granularity.

The stress testing factors evaluated by a stress test may change basedon the test or the scenario tested. The stress testing factors evaluatedin the stress test may also result in an algorithm or QC algorithm beingselected for the stress test based on the algorithms performance orother factors. The factors may or may not be dependent on the scenariobeing tested, the reconciliation of risk data (e.g., the output of astress test) with other financial data (e.g., financial data from ageneral ledger), data availability (including at what level orgranularity data is available), data gaps, and/or if the data is sorted,unsorted, correlated, or uncorrelated.

In some embodiments, the stress testing may interact with authorities ina company, such as a supervisory authority that may supervise but notrun the stress testing. The stress test may generate triggers associatedwith the supervising authority. In some embodiments, triggers may alertthe supervising authority or request information from the supervisingauthority. In some embodiments, if a data gap is identified, theappropriate granularity of data is not available, a modelling choice isrequired, and/or an interpretive issue is identified (e.g., how tointerpret a scenario, a model, and/or data), the stress test maygenerate a trigger that alerts and/or request the supervising authorityto provide input related to the trigger.

In some embodiments, a stress test may have a threshold that may berequired to indicate that the stress test was passed (e.g., greater thanzero or greater than or equal to zero) or that the stress test wasfailed (e.g., less than zero). Based on if the stress test passes orfails, the stress test may generate a trigger. In some embodiments, sucha trigger may cause the stress test to perform reverse stress testing tovary scenarios, models, factors, and/or algorithms to identify why astress test failed. In one embodiment, the stress testing of a pluralityof factors resulting in a failure of the stress test to return a valueabove a threshold may result in the stress test being rerun by varyingeach of the factors until the stress test is passed. In otherembodiments, the stress test may be run in reverse without needing topass or fail a first stress test, which may assist in identifyingsensitivities or vulnerabilities to the scenarios, models, factors, oralgorithms used in stress testing.

In some embodiments, the stress testing may be dynamic. Dynamic stresstesting may be updated as scenarios, models, factors, or dataavailability change. These updates may be at specific time periods(e.g., hours, days, weeks, months, quarters, and/or years) and/or basedon availability. In some embodiments, availability may be determined bythe stress testing monitoring a, for example, data source and, upon adetermination updated data is available, the stress testing will receivethe updated data from the data source and incorporate the updated datainto the stress testing to provide an updated result. In someembodiments, the stress testing may include multiple assumptions, suchas a future performance of an asset, and the stress testing may bedynamic by, at the appropriate period in the future, updating theassumption with actual performance of the asset, which may or may notrequire updating a scenario, a model, a factor, or an algorithm,including but not limited to selecting a new scenario, model, factor, oralgorithm. The updating of a dynamic model may or may not requirealerting and approval from a supervisory authority.

In FIG. 7 , as shown by operation 702, the apparatus 200 includes means,such as communications circuitry 208 and/or input-output circuitry 206or the like, for receiving stress testing scenario data. The stresstesting scenario data may include optimization factor data selected fora scenario, which may further include values, ranges, or thresholds foreach of the selected optimization factor data.

As shown by operation 704, the apparatus 200 includes means, such ascode identification circuitry 230 or the like, for identifying filteredstress testing factor data based on one or more of stress testing factordata, stress testing scenario data, QC algorithms, algorithm performanceinformation, and code performance information. The filtering may filterthe stress testing factor data and/or stress testing scenario data fordata that will be used with or by a QC algorithm. Additionally, oralternatively, the filtering may filter the stress testing factor dataand/or stress testing scenario data for data that will not be used withor by a QC algorithm.

The one or more QC algorithms may be one or more of, for example,Quadratic Unconstrained Binary Optimization “QUBO”, Quantum ApproximateOptimization Algorithm “QAOA”, Quantum Machine Learning “QML”, QuantumGeometrodynamics “QGD”, Quantum Monte Carlo “QMC”, Harrow, Hassidim andLloyd “HHL”, or the like, executed on a particular QC machine (e.g.,quantum annealer, circuit-based quantum processor, or the like).

As shown by operation 706, the apparatus 200 includes means, such asalgorithm selection circuitry 234 or the like, for selecting one or moreQC algorithms for the filtered stress testing factor data for generatinga stress testing determination. In some embodiments, the algorithmselection circuitry 234 may receive results of comparing the filteredstress testing factor data with performance of one or more QC algorithmsand/or performance of code, and a QC algorithm will be selectedaccordingly.

As shown by operation 708, the apparatus 200 includes means, such as QCoptimization circuitry 228, for utilizing the selected QC algorithm(s)to generate a stress testing determination from the filtered stresstesting factor data which determines the results of the stress test,such as how a company being stress tested performed. A stress testdetermination, such as for CCAR, may provide what was determinedregarding how a company performed in regards to: its current regulatorycapital base; planned capital actions; processes and policies fordetermining the size of dividend and common stock repurchase programsunder different operating conditions; potential losses, earnings, andother resources available to absorb losses; and an assessment of thecapital needed to continue operations, meet its obligations, andfunction as a credit intermediary.

Additionally, or alternatively, a generation of a stress testingdetermination may also include the generation of information supportingthe stress testing determination, including how a company was determinedto have performed under stress testing scenarios, which may be a report.

Hurricane Graphics

FIG. 8 is a flowchart showing exemplary operations for performing ahurricane determination based on QC algorithms in accordance with someexample embodiments described herein. A customer, who may or may not bethe portfolio owner, may be presented with a graphical user interfacepresenting a plurality of information about a portfolio. The graphicaluser interface may include a hurricane GUI, which may provide multipledifferent outcomes related to the portfolio and also may allow thecustomer to choose one of the outcomes or provide feedback regarding theoutcomes. The outcomes may be generated from optimizations, forecasts,and or stress testing, including how the portfolio may perform invarious scenarios. The outcomes may be personalized to the customer, tothe portfolio, or a portion of the portfolio.

The hurricane GUI may be based on a hurricane model. A hurricane modelmay provide a plurality of different risk tolerances (e.g., aggressive,conservative, etc.) and/or investment goals (e.g., growth, income,etc.). The hurricane model may, as described above, optimize, forecast,and/or stress test the portfolio according to the hurricane model.

The hurricane model may be applied to the portfolio using QC algorithms,which may be selected based on, among other things, data to be used inthe hurricane model, algorithm performance information, and/or QC costs.A single QC algorithm may be used for the hurricane model, or aplurality of QC algorithms may be used. If a plurality of QC algorithmsis used, a QC algorithm may be selected and used for distinct data inthe hurricane model.

The hurricane GUI may display the results of the hurricane model to thecustomer, and it may provide insights and/or suggestions related toportfolio decisions that may be taken by a customer. The customer maychoose one or more actions to be taken based on the hurricane GUI, andthe action may be related to the entirety of the portfolio or to aportion of the portfolio. In an embodiment, the action that may bechosen may be to rebalance a portion or all of the portfolio, which maybe related optimizing the portfolio for set of factors addressed in thehurricane model.

In one embodiment, a hurricane model may address a conservative risklevel and anticipates a recession or a world event that negativelyimpacts the portfolio performance. The hurricane model may evaluate thecustomer's risk level in the scenario by forecasting how the scenarioimpacts, among other things, interest rates, stock prices, and bondprices. The model may filter the factors that be used to forecastinterest rates, stock prices, and bond prices based on factors relatedto interest rates, stock prices, and bond prices and based on QCalgorithms and QC algorithm performance. The filtering may includedetermining how the factors may change and if the factors may change. AQC algorithm may be selected for each of the filtered factors or,alternatively, a QC algorithm may be selected that addresses all of thefiltered factors. The selected QC algorithms may then be used, and theoutput may be displayed in a hurricane GUI. A customer may then choosean action to take from the hurricane GUI.

In FIG. 8 , as shown by operation 802, the apparatus 200 includes means,such as communications circuitry 208 and/or input-output circuitry 206or the like, for receiving hurricane scenario data. The stress testingscenario data may include optimization factor data selected for ascenario, which may further include values, ranges, or thresholds foreach of the selected optimization factor data.

As shown by operation 804, the apparatus 200 includes means, such ascode identification circuitry 230 or the like, for identifying filteredhurricane factor data based on one or more of optimization factor data,hurricane scenario data, QC algorithms, algorithm performanceinformation, and code performance information. The filtering may includefiltering the optimization factor data and/or hurricane scenario datafor data that will be used with or by a QC algorithm. Additionally, oralternatively, the filtering may include filtering the optimizationfactor data and/or hurricane scenario data for data that will not beused with or by a QC algorithm.

The one or more QC algorithms may be one or more of, for example,Quadratic Unconstrained Binary Optimization “QUBO”, Quantum ApproximateOptimization Algorithm “QAOA”, Quantum Machine Learning “QML”, QuantumGeometrodynamics “QGD”, Quantum Monte Carlo “QMC”, Harrow, Hassidim andLloyd “HHL”, or the like, executed on a particular QC machine (e.g.,quantum annealer, circuit-based quantum processor, or the like).

As shown by operation 806, the apparatus 200 includes means, such asalgorithm selection circuitry 234 or the like, for selecting one or moreQC algorithms for the filtered hurricane factor data for generating ahurricane determination. In some embodiments, the algorithm selectioncircuitry 234 may receive results of comparing the filtered hurricanefactor data with performance of one or more QC algorithms and/orperformance of code, and a QC algorithm will be selected accordingly.

As shown by operation 808, the apparatus 200 includes means, such as QCoptimization circuitry 228, for utilizing the selected QC algorithm(s)to generate a hurricane determination from the filtered hurricane factordata which determines, such as how a portfolio performed for a hurricanescenario. A hurricane determination may provide what was determinedregarding how a portfolio performed and may be displayed via a hurricaneGUI to a customer to present multiple difference outcomes related to aportfolio, which may allow a customer to choose one of the outcomesand/or provide feedback.

Conclusion

While various embodiments in accordance with the principles disclosedherein have been shown and described above, modifications thereof may bemade by one skilled in the art without departing from the teachings ofthe disclosure. The embodiments described herein are representative onlyand are not intended to be limiting. Many variations, combinations, andmodifications are possible and are within the scope of the disclosure.Alternative embodiments that result from combining, integrating, and/oromitting features of the embodiment(s) are also within the scope of thedisclosure. Accordingly, the scope of protection is not limited by thedescription set out above, but is defined by the claims which follow,that scope including all equivalents of the subject matter of theclaims. Each and every claim is incorporated as further disclosure intothe specification and the claims are embodiment(s) of the presentdisclosure. Furthermore, any advantages and features described above mayrelate to specific embodiments but shall not limit the application ofsuch issued claims to processes and structures accomplishing any or allof the above advantages or having any or all of the above features.

In addition, the section headings used herein are provided forconsistency with the suggestions under 37 C.F.R. § 1.77 or to otherwiseprovide organizational cues. These headings shall not limit orcharacterize the disclosure set out in any claims that may issue fromthis disclosure. For instance, a description of a technology in the“Background” is not to be construed as an admission that certaintechnology is prior art to any disclosure in this disclosure. Neither isthe “Summary” to be considered as a limiting characterization of thedisclosure set forth in issued claims. Furthermore, any reference inthis disclosure to “disclosure” or “embodiment” in the singular shouldnot be used to argue that there is only a single point of novelty inthis disclosure. Multiple embodiments of the present disclosure may beset forth according to the limitations of the multiple claims issuingfrom this disclosure, and such claims accordingly define the disclosure,and their equivalents, that are protected thereby. In all instances, thescope of the claims shall be considered on their own merits in light ofthis disclosure but should not be constrained by the headings set forthherein.

Also, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other devices or components shown or discussed as coupled to, or incommunication with, each other may be indirectly coupled through someintermediate device or component, whether electrically, mechanically, orotherwise. Other examples of changes, substitutions, and alterations areascertainable by one skilled in the art and could be made withoutdeparting from the scope disclosed herein.

Many modifications and other embodiments of the disclosure set forthherein will come to mind to one skilled in the art to which theseembodiments pertain having the benefit of teachings presented in theforegoing descriptions and the associated figures. Although the figuresonly show certain components of the apparatus and systems describedherein, it is understood that various other components may be used inconjunction with the QC system. Therefore, it is to be understood thatthe disclosure is not to be limited to the specific embodimentsdisclosed and that modifications and other embodiments are intended tobe included within the scope of the appended claims. For example, thevarious elements or components may be combined, rearranged, orintegrated in another system or certain features may be omitted or notimplemented. Moreover, the steps in any method described above may notnecessarily occur in the order depicted in the accompanying figures, andin some cases one or more of the steps depicted may occur substantiallysimultaneously, or additional steps may be involved. Although specificterms are employed herein, they are used in a generic and descriptivesense only and not for purposes of limitation.

What is claimed is:
 1. A system for quantum computing (QC) basedoptimization of a personalized portfolio, the system comprising: QCoptimization factor filtering circuitry configured to identify aplurality of filtered personalized portfolio optimization factor databased on one or more optimization factor data for the personalizedportfolio, personalized portfolio owner feedback, QC algorithms, andalgorithm performance information; algorithm selection circuitryconfigured to select, automatically, one QC algorithm from a set of QCalgorithms for each filtered personalized portfolio optimization factordata of the plurality of filtered personalized portfolio optimizationfactor data; QC optimization circuitry configured to utilize theselected QC algorithm to optimize a personalized portfolio determinationfor each identified filtered personalized portfolio optimization factordata; and processing circuitry configured to rebalance the personalizedportfolio based on the personalized portfolio determination.
 2. Thesystem of claim 1, further comprising input-output circuitry configuredto receive portfolio owner constraints related to the personalizedportfolio, and wherein the portfolio owner constraints are included inthe personalized portfolio owner feedback.
 3. The system of claim 1,further comprising input-output circuitry configured to provide apersonalized portfolio owner with a rebalancing alert based on thepersonalized portfolio determination.
 4. The system of claim 3, whereinthe processing circuitry is further configured to, after receivingadditional feedback from the personalized portfolio owner, rebalance thepersonalized portfolio associated with the personalized portfolio ownerbased on the additional feedback and the personalized portfoliodetermination.
 5. The system of claim 1, wherein the algorithm selectioncircuitry is further configured to receive a catalog of QC algorithmsand associated algorithm performance information.
 6. The system of claim1, wherein the optimization factor data comprises one or more ofpersonal portfolio constraints data representing customer-definedconstraints.
 7. The system of claim 1, wherein the algorithm selectioncircuitry configured to select one QC algorithm is further configured toselect QC algorithms based on a QC run cost.
 8. A method for quantumcomputing (QC) based optimization of a personalized portfolio, themethod comprising: identifying, by QC optimization factor filteringcircuitry, a plurality of filtered personalized portfolio optimizationfactor data based on one or more optimization factor data for thepersonalized portfolio, personalized portfolio owner feedback, QCalgorithms, and algorithm performance information; selecting, byalgorithm selection circuitry, automatically, one QC algorithm from aset of QC algorithms for each filtered personalized portfoliooptimization factor data of the plurality of filtered personalizedportfolio optimization factor data; utilizing, by QC optimizationcircuitry, the selected QC algorithm to optimize a personalizedportfolio determination for each identified filtered personalizedportfolio optimization factor data; rebalancing the personalizedportfolio based on the personalized portfolio determination.
 9. Themethod of claim 8, further comprising receiving portfolio ownerconstraints related to the personalized portfolio, and wherein theportfolio owner constraints are included in the personalized portfolioowner feedback.
 10. The method of claim 8, further comprising providinga personalized portfolio owner with a rebalancing alert based on thepersonalized portfolio determination.
 11. The method of claim 10,further comprising, after receiving additional feedback from thepersonalized portfolio owner, rebalancing the personalized portfolioassociated with the personalized portfolio owner based on the additionalfeedback and the personalized portfolio determination.
 12. The method ofclaim 8, further comprising receiving a catalog of QC algorithms andassociated algorithm performance information.
 13. The method of claim 8,wherein the optimization factor data comprises one or more of personalportfolio constraints data representing customer-defined constraints.14. The method of claim 8, wherein the selecting one QC algorithm isbased on a QC run cost.
 15. The method of claim 8, further comprising:identifying, by code identification circuitry, runtime hotspotsassociated with personalized portfolio optimization factor data thatwould benefit from QC, wherein identifying the plurality of filteredportfolio optimization factor data is further based on the identifiedruntime hotspots.
 16. A computer program product for quantum computing(QC) based optimization of a personalized portfolio, the computerprogram product comprising at least one non-transitory computer-readablestorage medium storing program instructions that, when executed, cause asystem to: identify a plurality of filtered personalized portfoliooptimization factor data based on one or more optimization factor datafor the personalized portfolio, personalized portfolio owner feedback,QC algorithms, and algorithm performance information; select,automatically, one QC algorithm from a set of QC algorithms for eachfiltered portfolio personalized optimization factor data of theplurality of filtered personalized portfolio optimization factor data;utilize the selected QC algorithm to optimize a personalized portfoliodetermination for each identified filtered personalized portfoliooptimization factor data; and rebalance the personalized portfolio basedon the personalized portfolio determination.
 17. The computer programproduct of claim 16, wherein the program instructions, when executed,further cause the system to receive portfolio owner constraints relatedto the personalized portfolio, and wherein the portfolio ownerconstraints are included in the personalized portfolio owner feedback.18. The computer program product of claim 16, wherein the programinstructions, when executed, further cause the system to provide aportfolio owner with a rebalancing alert based on the personalizedportfolio determination.
 19. The computer program product of claim 16,wherein the program instructions, when executed, further cause thesystem, to receive a catalog of QC algorithms and associated algorithmperformance information.
 20. The computer program product of claim 16,wherein the portfolio optimization factor data comprises one or more ofpersonal portfolio constraints data representing customer-definedconstraints.